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math-inc/OpenGauss

Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.

CodexEngineering

History

Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.

View weekly changes

optional-skills/autonomous-ai-agents/blackbox/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/blockchain/solana/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/creative/blender-mcp/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/email/agentmail/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Local scripts → After Local scripts

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Local scripts

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/health/neuroskill-bci/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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optional-skills/migration/openclaw-migration/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Local scripts → After Local scripts

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Local scripts

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/productivity/telephony/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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optional-skills/research/qmd/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/security/1password/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

optional-skills/security/oss-forensics/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/apple/apple-notes/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/apple/apple-reminders/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/apple/findmy/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/apple/imessage/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/autonomous-ai-agents/claude-code/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/autonomous-ai-agents/codex/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/autonomous-ai-agents/gauss-agent/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/autonomous-ai-agents/opencode/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/creative/ascii-art/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/creative/ascii-video/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/creative/excalidraw/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/data-science/jupyter-live-kernel/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/dogfood/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/email/himalaya/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/gaming/minecraft-modpack-server/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/gaming/pokemon-player/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/github/codebase-inspection/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/github/github-auth/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/github/github-code-review/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/github/github-issues/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/github/github-pr-workflow/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/github/github-repo-management/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/leisure/find-nearby/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mcp/mcporter/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mcp/native-mcp/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/media/gif-search/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/media/heartmula/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/media/songsee/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

Compare on GitHub

skills/media/youtube-content/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Local scripts → After Local scripts

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Local scripts

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/cloud/lambda-labs/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/cloud/modal/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/evaluation/huggingface-tokenizers/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/evaluation/lm-evaluation-harness/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/evaluation/nemo-curator/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/evaluation/saelens/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/evaluation/weights-and-biases/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/gguf/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/guidance/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/instructor/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/llama-cpp/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/obliteratus/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/outlines/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/tensorrt-llm/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/inference/vllm/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/audiocraft/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/clip/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/llava/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/segment-anything/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/stable-diffusion/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/models/whisper/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/research/dspy/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/accelerate/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/axolotl/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Network or broad shell → After Network or broad shell

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/flash-attention/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/gauss-atropos-environments/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

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f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Network or broad shell

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/grpo-rl-training/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Local scripts → After Local scripts

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Local scripts

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/peft/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/pytorch-fsdp/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/pytorch-lightning/SKILL.md

Week of Nov 2, 2026 · +0 / −0 lines

Before Instructions only → After Instructions only

f87633900ae1 → f87633900ae1

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Week of Oct 26, 2026 · Line counts unavailable

Before Not checked yet → After Instructions only

First observation → f87633900ae1

No earlier version was observed for comparison.

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skills/mlops/training/simpo/SKILL.md

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skills/mlops/training/slime/SKILL.md

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skills/mlops/training/torchtitan/SKILL.md

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skills/mlops/training/trl-fine-tuning/SKILL.md

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skills/mlops/training/unsloth/SKILL.md

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skills/mlops/vector-databases/chroma/SKILL.md

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skills/mlops/vector-databases/faiss/SKILL.md

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skills/mlops/vector-databases/pinecone/SKILL.md

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skills/mlops/vector-databases/qdrant/SKILL.md

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skills/note-taking/obsidian/SKILL.md

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skills/productivity/google-workspace/SKILL.md

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skills/productivity/linear/SKILL.md

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skills/productivity/nano-pdf/SKILL.md

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skills/productivity/notion/SKILL.md

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skills/productivity/ocr-and-documents/SKILL.md

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skills/productivity/powerpoint/SKILL.md

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skills/research/arxiv/SKILL.md

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skills/research/blogwatcher/SKILL.md

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skills/research/domain-intel/SKILL.md

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skills/research/duckduckgo-search/SKILL.md

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skills/research/ml-paper-writing/SKILL.md

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skills/research/parallel-cli/SKILL.md

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skills/research/polymarket/SKILL.md

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skills/smart-home/openhue/SKILL.md

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skills/social-media/xitter/SKILL.md

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skills/software-development/code-review/SKILL.md

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skills/software-development/plan/SKILL.md

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skills/software-development/requesting-code-review/SKILL.md

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skills/software-development/subagent-driven-development/SKILL.md

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skills/software-development/systematic-debugging/SKILL.md

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skills/software-development/test-driven-development/SKILL.md

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skills/software-development/writing-plans/SKILL.md

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View on GitHubAdd to my skills

Verified commit f87633900ae185b8037bf451a914fe7eeae1eb08

git clone --filter=blob:none --no-checkout https://github.com/math-inc/OpenGauss.git .hotskills/math-inc-opengauss && git -C .hotskills/math-inc-opengauss checkout --detach f87633900ae185b8037bf451a914fe7eeae1eb08 # Copy each selected skill directory from .hotskills/math-inc-opengauss into .agents/skills/<skill-name>/
1.3kGitHub stars
1star in 7 days
101skills in this repository
0commits in 30 days

Heat

1.5
  • Star growth, last 7 daysAhead of 36% of tracked repositories
  • Star growth, last 24 hoursAhead of 3% of tracked repositories
  • Rank movement, last 7 daysAhead of 5% of tracked repositories
  • Commits, last 30 daysAhead of 0% of tracked repositories
  • Accounts asking about it through MCP, last 7 daysNot available
  • Hot votes this weekAhead of 0% of tracked repositories

Heat never changes the order by stars, and sponsorship never touches it. How heat works

Stars over time

Aug 2, 2026Nov 3, 20261,276

Individual skills in this repository

How it works

blackbox

When your agent uses it

Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.

What it loads into context

Frontmatter

115 tokens

Always available to the agent

SKILL.md

1,313 tokens

When the skill is activated

Reference documents

0 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

  1. Prerequisites
  2. One-Shot Tasks
  3. Background Mode (Long Tasks)
  4. Checkpoints & Resume
  5. Session Commands
  6. PR Reviews
  7. Parallel Work
  8. Multi-Model Mode
  9. Key Flags
  10. Vision Support
  11. Token Limits
  12. Rules

Files

  • SKILL.mdcore5,396 bytes
Read SKILL.md

# Blackbox CLI

Delegate coding tasks to [Blackbox AI](https://www.blackbox.ai/) via the Gauss terminal. Blackbox is a multi-model coding agent CLI that dispatches tasks to multiple LLMs (Claude, Codex, Gemini, Blackbox Pro) and uses a judge to select the best implementation.

The CLI is [open-source](https://github.com/blackboxaicode/cli) (GPL-3.0, TypeScript, forked from Gemini CLI) and supports interactive sessions, non-interactive one-shots, checkpointing, MCP, and vision model switching.

## Prerequisites

- Node.js 20+ installed
- Blackbox CLI installed: `npm install -g @blackboxai/cli`
- Or install from source:
  ```
  git clone https://github.com/blackboxaicode/cli.git
  cd cli && npm install && npm install -g .
View the complete source on GitHub

Skills included

  • blackbox
  • solana
  • blender-mcp
  • agentmail
  • neuroskill-bci
  • openclaw-migration
  • telephony
  • qmd
  • 1password
  • oss-forensics
  • apple-notes
  • apple-reminders
  • findmy
  • imessage
  • claude-code
  • codex
  • gauss-agent-spawning
  • opencode
  • ascii-art
  • ascii-video
  • excalidraw
  • jupyter-live-kernel
  • dogfood
  • himalaya
  • minecraft-modpack-server
  • pokemon-player
  • codebase-inspection
  • github-auth
  • github-code-review
  • github-issues
  • github-pr-workflow
  • github-repo-management
  • find-nearby
  • mcporter
  • native-mcp
  • gif-search
  • heartmula
  • songsee
  • youtube-content
  • lambda-labs-gpu-cloud
  • modal-serverless-gpu
  • huggingface-tokenizers
  • evaluating-llms-harness
  • nemo-curator
  • sparse-autoencoder-training
  • weights-and-biases
  • gguf-quantization
  • guidance
  • instructor
  • llama-cpp
  • obliteratus
  • outlines
  • tensorrt-llm
  • serving-llms-vllm
  • audiocraft-audio-generation
  • clip
  • llava
  • segment-anything-model
  • stable-diffusion-image-generation
  • whisper
  • dspy
  • huggingface-accelerate
  • axolotl
  • optimizing-attention-flash
  • gauss-atropos-environments
  • grpo-rl-training
  • peft-fine-tuning
  • pytorch-fsdp
  • pytorch-lightning
  • simpo-training
  • slime-rl-training
  • distributed-llm-pretraining-torchtitan
  • fine-tuning-with-trl
  • unsloth
  • chroma
  • faiss
  • pinecone
  • qdrant-vector-search
  • obsidian
  • google-workspace
  • linear
  • nano-pdf
  • notion
  • ocr-and-documents
  • powerpoint
  • arxiv
  • blogwatcher
  • domain-intel
  • duckduckgo-search
  • ml-paper-writing
  • parallel-cli
  • polymarket
  • openhue
  • xitter
  • code-review
  • plan
  • requesting-code-review
  • subagent-driven-development
  • systematic-debugging
  • test-driven-development
  • writing-plans

Repository facts

License
MIT
Created
Mar 19, 2026
Last pushed
Apr 5, 2026
Forks
113
Open issues
9
Project activity
Slowing

Rank over the last 12 weeks

Now #740 · best #477 · down 164 in 30 days

  1. #726 on Nov 1
  2. #728 on Nov 2
  3. #733 on Nov 3
  4. #735 on Nov 4
  5. #737 on Nov 5
  6. #740 since Nov 6
33 earlier periods
  1. #470 on Aug 2
  2. #483 on Aug 7
  3. #478 on Aug 8
  4. #476 on Aug 9
  5. #495 on Aug 11
  6. #491 on Aug 12
  7. #497 on Aug 13
  8. #477 on Aug 15
  9. #488 on Aug 16
  10. #493 on Aug 17
  11. #502 on Aug 21
  12. #516 on Aug 25
  13. #520 on Aug 27
  14. #500 on Aug 30
  15. #522 on Sep 2
  16. #540 on Sep 10
  17. #550 on Sep 16
  18. #551 on Sep 18
  19. #565 on Sep 23
  20. #573 on Sep 28
  21. #576 on Oct 5
  22. #618 on Oct 8
  23. #614 on Oct 13
  24. #626 on Oct 14
  25. #631 on Oct 16
  26. #647 on Oct 20
  27. #655 on Oct 21
  28. #637 from Oct 22 to Oct 23
  29. #704 on Oct 25
  30. #711 on Oct 28
  31. #714 on Oct 29
  32. #719 on Oct 30
  33. #724 on Oct 31

Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 3, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

Show 1,292 notes
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • optional-skills/blockchain/solana/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
Show all 6,162 signals by file

gauss

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Executable scriptline 1
    #!/usr/bin/env python3
  • Executable scriptline 1
    #!/usr/bin/env python3

optional-skills/autonomous-ai-agents/blackbox/SKILL.md

  • Network accessline 15
    Delegate coding tasks to [Blackbox AI](https://www.blackbox.ai/) via the Gauss terminal. Blackbox is a multi-model coding agent CLI that dispatches tasks to multiple LLMs (Claude, Codex, Gemini, Blackbox Pro) and uses a judge to select the
  • Network accessline 17
    The CLI is [open-source](https://github.com/blackboxaicode/cli) (GPL-3.0, TypeScript, forked from Gemini CLI) and supports interactive sessions, non-interactive one-shots, checkpointing, MCP, and vision model switching.
  • Network accessline 25
    git clone https://github.com/blackboxaicode/cli.git
  • Network accessline 28
    - API key from [app.blackbox.ai/dashboard](https://app.blackbox.ai/dashboard)
  • Network accessline 89
    terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && blackbox --prompt 'Review this PR against main. Check for bugs, security issues, and code quality.'", pty=true

optional-skills/blockchain/solana/SKILL.md

  • Network accessline 48
    RPC endpoint (default): https://api.mainnet-beta.solana.com
  • Network accessline 49
    Override: export SOLANA_RPC_URL=https://your-private-rpc.com
  • Network accessline 73
    # Optional: set a private RPC for better rate limits export SOLANA_RPC_URL="https://api.mainnet-beta.solana.com"
  • Network accessline 74
    export SOLANA_RPC_URL="https://api.mainnet-beta.solana.com"

optional-skills/blockchain/solana/scripts/solana_client.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 31
    [Credential-related source omitted]
  • Access to secretsline 31
    [Credential-related source omitted]
  • Network accessline 32
    "SOLANA_RPC_URL",     "https://api.mainnet-beta.solana.com", )
  • Network accessline 33
    "https://api.mainnet-beta.solana.com",
  • Network accessline 76
    for attempt in range(retries + 1):         req = urllib.request.Request(
  • Network accessline 77
    req = urllib.request.Request(
  • Network accessline 79
    )         try:             with urllib.request.urlopen(req, timeout=timeout) as resp:
  • Network accessline 80
    try:             with urllib.request.urlopen(req, timeout=timeout) as resp:                 return json.load(resp)
  • Network accessline 81
    with urllib.request.urlopen(req, timeout=timeout) as resp:
  • Network accessline 100
    for attempt in range(retries + 1):         req = urllib.request.Request(
  • Network accessline 101
    req = urllib.request.Request(
  • Network accessline 104
    )         try:             with urllib.request.urlopen(req, timeout=20) as resp:
  • Network accessline 105
    try:             with urllib.request.urlopen(req, timeout=20) as resp:                 body = json.load(resp)
  • Network accessline 106
    with urllib.request.urlopen(req, timeout=20) as resp:
  • Network accessline 138
    for attempt in range(3):         req = urllib.request.Request(
  • Network accessline 139
    req = urllib.request.Request(
  • Network accessline 142
    )         try:             with urllib.request.urlopen(req, timeout=20) as resp:
  • Network accessline 143
    try:             with urllib.request.urlopen(req, timeout=20) as resp:                 return json.load(resp)
  • Network accessline 144
    with urllib.request.urlopen(req, timeout=20) as resp:
  • Network accessline 183
    for i, mint in enumerate(mints[:max_lookups]):         url = (             f"https://api.coingecko.com/api/v3/simple/token_price/solana"
  • Network accessline 184
    url = (             f"https://api.coingecko.com/api/v3/simple/token_price/solana"             f"?contract_addresses={mint}&vs_currencies=usd"
  • Network accessline 185
    f"https://api.coingecko.com/api/v3/simple/token_price/solana"
  • Network accessline 202
    data = _http_get_json(         "https://api.coingecko.com/api/v3/simple/price?ids=solana&vs_currencies=usd"
  • Network accessline 203
    "https://api.coingecko.com/api/v3/simple/price?ids=solana&vs_currencies=usd"
  • Network accessline 216
    sym, name = KNOWN_TOKENS[mint]         return {"symbol": sym, "name": name}     url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}"
  • Network accessline 217
    return {"symbol": sym, "name": name}     url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}"     data = _http_get_json(url, timeout=10)
  • Network accessline 218
    url = f"https://api.coingecko.com/api/v3/coins/solana/contract/{mint}"

optional-skills/creative/blender-mcp/SKILL.md

  • Network accessline 18
    curl -sL https://raw.githubusercontent.com/ahujasid/blender-mcp/main/addon.py -o ~/Desktop/blender_mcp_addon.py
  • Network accessline 31
    nc -z -w2 localhost 9876 && echo "OPEN" || echo "CLOSED"
  • Network accessline 56
    def blender_exec(code: str, host="localhost", port=9876, timeout=15):         s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
  • Network accessline 57
    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
  • Network accessline 112
    - Must check socket is open before running (nc -z localhost 9876)

optional-skills/email/agentmail/SKILL.md

  • Network accessline 15
    - **AgentMail API key** (required) — sign up at https://console.agentmail.to (free tier: 3 inboxes, 3,000 emails/month; paid plans from $20/mo)
  • Network accessline 32
    ### 1. Get an API Key - Go to https://console.agentmail.to
  • Network accessline 33
    - Go to https://console.agentmail.to
  • Access to secretsline 44
    [Credential-related source omitted]
  • Network accessline 121
    ## References - AgentMail docs: https://docs.agentmail.to/
  • Network accessline 122
    - AgentMail docs: https://docs.agentmail.to/
  • Network accessline 123
    - AgentMail console: https://console.agentmail.to
  • Network accessline 124
    - AgentMail MCP repo: https://github.com/agentmail-to/agentmail-mcp
  • Network accessline 125
    - Pricing: https://www.agentmail.to/pricing

optional-skills/health/neuroskill-bci/SKILL.md

  • Network accessline 21
    Connect Gauss to a running [NeuroSkill](https://neuroskill.com/) instance to read
  • Network accessline 455
    - [NeuroSkill Paper — arXiv:2603.03212](https://arxiv.org/abs/2603.03212) (Kosmyna & Hauptmann, MIT Media Lab)
  • Network accessline 456
    - [NeuroSkill Desktop App](https://github.com/NeuroSkill-com/skill) (GPLv3)
  • Network accessline 457
    - [NeuroLoop CLI Companion](https://github.com/NeuroSkill-com/neuroloop) (GPLv3)
  • Network accessline 458
    - [MIT Media Lab Project](https://www.media.mit.edu/projects/neuroskill/overview/)

optional-skills/health/neuroskill-bci/references/api.md

  • Network accessline 27
    # POST / — accepts any command as JSON curl -s -X POST http://127.0.0.1:8375/ \
  • Network accessline 28
    curl -s -X POST http://127.0.0.1:8375/ \

optional-skills/migration/openclaw-migration/scripts/openclaw_to_gauss.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Access to secretsline 38
    [Credential-related source omitted]
  • Access to secretsline 39
    [Credential-related source omitted]
  • Access to secretsline 40
    [Credential-related source omitted]
  • Access to secretsline 41
    [Credential-related source omitted]
  • Access to secretsline 100
    [Credential-related source omitted]
  • Access to secretsline 801
    [Credential-related source omitted]
  • Access to secretsline 817
    [Credential-related source omitted]
  • Access to secretsline 863
    [Credential-related source omitted]
  • Access to secretsline 874
    [Credential-related source omitted]
  • Access to secretsline 887
    [Credential-related source omitted]
  • Access to secretsline 896
    [Credential-related source omitted]
  • Access to secretsline 919
    [Credential-related source omitted]
  • Access to secretsline 941
    [Credential-related source omitted]
  • Access to secretsline 962
    [Credential-related source omitted]
  • Access to secretsline 977
    [Credential-related source omitted]
  • Access to secretsline 998
    [Credential-related source omitted]
  • Access to secretsline 1007
    [Credential-related source omitted]
  • Access to secretsline 1024
    [Credential-related source omitted]
  • Access to secretsline 1025
    [Credential-related source omitted]
  • Access to secretsline 1027
    [Credential-related source omitted]
  • Access to secretsline 1036
    [Credential-related source omitted]
  • Access to secretsline 1038
    [Credential-related source omitted]
  • Access to secretsline 1040
    [Credential-related source omitted]
  • Access to secretsline 1044
    [Credential-related source omitted]
  • Access to secretsline 1050
    [Credential-related source omitted]
  • Access to secretsline 1052
    [Credential-related source omitted]
  • Access to secretsline 1055
    [Credential-related source omitted]
  • Access to secretsline 1064
    [Credential-related source omitted]
  • Access to secretsline 1077
    [Credential-related source omitted]

optional-skills/productivity/telephony/SKILL.md

  • Access to secretsline 109
    [Credential-related source omitted]
  • Access to secretsline 112
    [Credential-related source omitted]
  • Access to secretsline 113
    [Credential-related source omitted]
  • Network accessline 150
    Sign up at: - https://www.twilio.com/try-twilio
  • Network accessline 151
    - https://www.twilio.com/try-twilio
  • Network accessline 187
    Sign up at: - https://app.bland.ai
  • Network accessline 188
    - https://app.bland.ai
  • Access to secretsline 193
    [Credential-related source omitted]
  • Network accessline 198
    Sign up at: - https://dashboard.vapi.ai
  • Network accessline 199
    - https://dashboard.vapi.ai
  • Access to secretsline 204
    [Credential-related source omitted]
  • Access to secretsline 216
    [Credential-related source omitted]
  • Network accessline 263
    python3 "$SCRIPT" twilio-send-sms "+15551230000" "Here is the chart." --media-url "https://example.com/chart.png"
  • Network accessline 300
    python3 "$SCRIPT" twilio-call "+155****0000" --audio-url "https://example.com/briefing.mp3"
  • Network accessline 413
    - Twilio phone numbers: https://www.twilio.com/docs/phone-numbers/api
  • Network accessline 414
    - Twilio messaging: https://www.twilio.com/docs/messaging/api/message-resource
  • Network accessline 415
    - Twilio voice: https://www.twilio.com/docs/voice/api/call-resource
  • Network accessline 416
    - Vapi docs: https://docs.vapi.ai/
  • Network accessline 417
    - Bland.ai: https://app.bland.ai/

optional-skills/productivity/telephony/scripts/telephony.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Access to secretsline 5
    [Credential-related source omitted]
  • Network accessline 35
    TWILIO_API_BASE = "https://api.twilio.com/2010-04-01/Accounts"
  • Network accessline 36
    VAPI_API_BASE = "https://api.vapi.ai"
  • Network accessline 37
    BLAND_API_BASE = "https://api.bland.ai/v1"
  • Access to secretsline 72
    [Credential-related source omitted]
  • Access to secretsline 76
    [Credential-related source omitted]
  • Access to secretsline 136
    [Credential-related source omitted]
  • Network accessline 259
    req = urllib.request.Request(url, data=body, headers=request_headers, method=method.upper())
  • Network accessline 260
    try:         with urllib.request.urlopen(req, timeout=30) as resp:             payload = resp.read().decode("utf-8")
  • Network accessline 261
    with urllib.request.urlopen(req, timeout=30) as resp:
  • Access to secretsline 282
    [Credential-related source omitted]
  • Access to secretsline 283
    [Credential-related source omitted]
  • Access to secretsline 284
    [Credential-related source omitted]
  • Access to secretsline 289
    [Credential-related source omitted]
  • Access to secretsline 429
    [Credential-related source omitted]
  • Access to secretsline 430
    [Credential-related source omitted]
  • Access to secretsline 431
    [Credential-related source omitted]
  • Access to secretsline 448
    [Credential-related source omitted]
  • Access to secretsline 449
    [Credential-related source omitted]
  • Access to secretsline 450
    [Credential-related source omitted]
  • Access to secretsline 756
    [Credential-related source omitted]
  • Access to secretsline 757
    [Credential-related source omitted]
  • Access to secretsline 759
    [Credential-related source omitted]
  • Access to secretsline 766
    [Credential-related source omitted]
  • Access to secretsline 803
    [Credential-related source omitted]
  • Access to secretsline 804
    [Credential-related source omitted]
  • Access to secretsline 806
    [Credential-related source omitted]
  • Access to secretsline 819
    [Credential-related source omitted]
  • Access to secretsline 846
    [Credential-related source omitted]
  • Access to secretsline 847
    [Credential-related source omitted]
  • Access to secretsline 849
    [Credential-related source omitted]
  • Access to secretsline 866
    [Credential-related source omitted]
  • Access to secretsline 881
    [Credential-related source omitted]
  • Access to secretsline 882
    [Credential-related source omitted]
  • Access to secretsline 884
    [Credential-related source omitted]
  • Access to secretsline 926
    [Credential-related source omitted]
  • Access to secretsline 949
    [Credential-related source omitted]
  • Access to secretsline 950
    [Credential-related source omitted]
  • Access to secretsline 955
    [Credential-related source omitted]
  • Access to secretsline 1008
    [Credential-related source omitted]
  • Access to secretsline 1009
    [Credential-related source omitted]
  • Access to secretsline 1010
    [Credential-related source omitted]
  • Access to secretsline 1079
    [Credential-related source omitted]
  • Access to secretsline 1082
    [Credential-related source omitted]
  • Access to secretsline 1101
    [Credential-related source omitted]
  • Access to secretsline 1104
    [Credential-related source omitted]
  • Access to secretsline 1112
    [Credential-related source omitted]
  • Access to secretsline 1119
    [Credential-related source omitted]
  • Access to secretsline 1127
    [Credential-related source omitted]
  • Access to secretsline 1154
    [Credential-related source omitted]
  • Access to secretsline 1156
    [Credential-related source omitted]
  • Access to secretsline 1160
    [Credential-related source omitted]
  • Access to secretsline 1161
    [Credential-related source omitted]
  • Access to secretsline 1164
    [Credential-related source omitted]
  • Access to secretsline 1165
    [Credential-related source omitted]
  • Access to secretsline 1238
    [Credential-related source omitted]
  • Access to secretsline 1240
    [Credential-related source omitted]
  • Access to secretsline 1243
    [Credential-related source omitted]

optional-skills/research/qmd/SKILL.md

  • Network accessline 21
    Created by [Tobi Lütke](https://github.com/tobi/qmd). MIT licensed.
  • Network accessline 43
    # Linux — use NodeSource or nvm curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
  • Dangerous shell operationline 44
    curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
  • Network accessline 44
    curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
  • Dangerous shell operationline 45
    sudo apt-get install -y nodejs
  • Network accessline 254
    # Runs on http://localhost:8181 by default
  • Network accessline 260
    mcp_servers:   qmd:     url: "http://localhost:8181/mcp"
  • Network accessline 261
    qmd:     url: "http://localhost:8181/mcp"     timeout: 30
  • Network accessline 262
    url: "http://localhost:8181/mcp"
  • Network accessline 275
    <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"   "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
  • Network accessline 276
    <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"   "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> <plist version="1.0">
  • Network accessline 277
    "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
  • Network accessline 440
    - [GitHub: tobi/qmd](https://github.com/tobi/qmd)
  • Network accessline 441
    - [QMD Changelog](https://github.com/tobi/qmd/blob/main/CHANGELOG.md)

optional-skills/security/1password/SKILL.md

  • Network accessline 10
    category: security setup:   help: "Create a service account at https://my.1password.com → Settings → Service Accounts"
  • Network accessline 11
    setup:   help: "Create a service account at https://my.1password.com → Settings → Service Accounts"   collect_secrets:
  • Network accessline 12
    help: "Create a service account at https://my.1password.com → Settings → Service Accounts"
  • Network accessline 14
    [Credential-related source omitted]
  • Access to secretsline 14
    [Credential-related source omitted]
  • Network accessline 15
    [Credential-related source omitted]
  • Network accessline 16
    provider_url: "https://developer.1password.com/docs/service-accounts/"
  • Access to secretsline 47
    [Credential-related source omitted]
  • Network accessline 60
    export OP_CONNECT_HOST="http://localhost:8080"
  • Access to secretsline 61
    [Credential-related source omitted]
  • Network accessline 159
    - `references/get-started.md` - `references/cli-examples.md` - https://developer.1password.com/docs/cli/
  • Network accessline 160
    - `references/cli-examples.md` - https://developer.1password.com/docs/cli/ - https://developer.1password.com/docs/service-accounts/
  • Network accessline 161
    - https://developer.1password.com/docs/cli/
  • Network accessline 162
    - https://developer.1password.com/docs/service-accounts/

optional-skills/security/1password/references/cli-examples.md

  • Access to secretsline 22
    [Credential-related source omitted]

optional-skills/security/1password/references/get-started.md

  • Network accessline 3
    Official docs: https://developer.1password.com/docs/cli/get-started/

optional-skills/security/oss-forensics/SKILL.md

  • Access to secretsline 99
    [Credential-related source omitted]
  • Network accessline 121
    # Clone repository git clone https://github.com/OWNER/REPO.git target_repo && cd target_repo
  • Network accessline 122
    git clone https://github.com/OWNER/REPO.git target_repo && cd target_repo
  • Network accessline 159
    # Commits (paginated) curl -s "https://api.github.com/repos/OWNER/REPO/commits?per_page=100" > api_commits.json
  • Network accessline 160
    curl -s "https://api.github.com/repos/OWNER/REPO/commits?per_page=100" > api_commits.json
  • Network accessline 162
    # Pull Requests including closed/deleted curl -s "https://api.github.com/repos/OWNER/REPO/pulls?state=all&per_page=100" > api_prs.json
  • Network accessline 163
    curl -s "https://api.github.com/repos/OWNER/REPO/pulls?state=all&per_page=100" > api_prs.json
  • Network accessline 165
    # Issues curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=all&per_page=100" > api_issues.json
  • Network accessline 166
    curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=all&per_page=100" > api_issues.json
  • Network accessline 168
    # Contributors and collaborator changes curl -s "https://api.github.com/repos/OWNER/REPO/contributors" > api_contributors.json
  • Network accessline 169
    curl -s "https://api.github.com/repos/OWNER/REPO/contributors" > api_contributors.json
  • Network accessline 171
    # Repository events (last 300) curl -s "https://api.github.com/repos/OWNER/REPO/events?per_page=100" > api_events.json
  • Network accessline 172
    curl -s "https://api.github.com/repos/OWNER/REPO/events?per_page=100" > api_events.json
  • Network accessline 174
    # Check specific suspicious commit SHA details curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" > commit_detail.json
  • Network accessline 175
    curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" > commit_detail.json
  • Network accessline 177
    # Releases curl -s "https://api.github.com/repos/OWNER/REPO/releases?per_page=100" > api_releases.json
  • Network accessline 178
    curl -s "https://api.github.com/repos/OWNER/REPO/releases?per_page=100" > api_releases.json
  • Network accessline 180
    # Check if a specific commit exists (force-pushed commits may 404 on commits/ but succeed on git/commits/) curl -s "https://api.github.com/repos/OWNER/REPO/commits/SHA" | jq .sha
  • Network accessline 181
    curl -s "https://api.github.com/repos/OWNER/REPO/commits/SHA" | jq .sha
  • Network accessline 201
    # Search for archived snapshots of the repo main page curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO&output=json&limit=100&from=YYYYMMDD&to=YYYYMMDD" > wayback_main.json
  • Network accessline 202
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO&output=json&limit=100&from=YYYYMMDD&to=YYYYMMDD" > wayback_main.json
  • Network accessline 204
    # Search for a specific deleted issue curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUM&output=json&limit=50" > wayback_issue_NUM.json
  • Network accessline 205
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUM&output=json&limit=50" > wayback_issue_NUM.json
  • Network accessline 207
    # Search for a specific deleted PR curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/pull/NUM&output=json&limit=50" > wayback_pr_NUM.json
  • Network accessline 208
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/pull/NUM&output=json&limit=50" > wayback_pr_NUM.json
  • Network accessline 210
    # Fetch the best snapshot of a page # Use the Wayback Machine URL: https://web.archive.org/web/TIMESTAMP/ORIGINAL_URL
  • Network accessline 211
    # Use the Wayback Machine URL: https://web.archive.org/web/TIMESTAMP/ORIGINAL_URL
  • Network accessline 212
    # Example: https://web.archive.org/web/20240101000000*/github.com/OWNER/REPO
  • Network accessline 214
    # Advanced: Search for deleted releases/tags curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/releases/tag/*&output=json" > wayback_tags.json
  • Network accessline 215
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/releases/tag/*&output=json" > wayback_tags.json
  • Network accessline 217
    # Advanced: Search for historical wiki changes curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/wiki/*&output=json" > wayback_wiki.json
  • Network accessline 218
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/wiki/*&output=json" > wayback_wiki.json
  • Access to secretsline 404
    [Credential-related source omitted]

optional-skills/security/oss-forensics/references/evidence-types.md

  • Access to secretsline 29
    [Credential-related source omitted]
  • Network accessline 33
    | `PACKAGE_NAME` | A malicious or squatted package name | `colo-rs` (typosquatting `color`), `lodash-utils` | | `ACTOR_USERNAME` | A GitHub handle linked to the attack | `malicious-bot-account` | | `MALICIOUS_URL` | A URL to a malicious res
  • Network accessline 34
    | `ACTOR_USERNAME` | A GitHub handle linked to the attack | `malicious-bot-account` | | `MALICIOUS_URL` | A URL to a malicious resource | `https://evil.example.com/payload.sh` | | `WORKFLOW_FILE` | A suspicious CI/CD workflow file | `.githu
  • Network accessline 35
    | `MALICIOUS_URL` | A URL to a malicious resource | `https://evil.example.com/payload.sh` |

optional-skills/security/oss-forensics/references/recovery-techniques.md

  • Network accessline 14
    # View commit metadata curl -s "https://github.com/OWNER/REPO/commit/SHA"
  • Network accessline 15
    curl -s "https://github.com/OWNER/REPO/commit/SHA"
  • Network accessline 17
    # Download as patch (includes full diff) curl -s "https://github.com/OWNER/REPO/commit/SHA.patch" > recovered_commit.patch
  • Network accessline 18
    curl -s "https://github.com/OWNER/REPO/commit/SHA.patch" > recovered_commit.patch
  • Network accessline 20
    # Download as diff curl -s "https://github.com/OWNER/REPO/commit/SHA.diff" > recovered_commit.diff
  • Network accessline 21
    curl -s "https://github.com/OWNER/REPO/commit/SHA.diff" > recovered_commit.diff
  • Network accessline 23
    # Example (Istio credential leak - real incident): curl -s "https://github.com/istio/istio/commit/FORCE_PUSHED_SHA.patch"
  • Network accessline 24
    curl -s "https://github.com/istio/istio/commit/FORCE_PUSHED_SHA.patch"
  • Network accessline 35
    # Works for commits force-pushed off branches but still on server # Note: /commits/SHA may 404, but /git/commits/SHA often succeeds for orphaned commits curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" | jq .
  • Network accessline 37
    curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" | jq .
  • Network accessline 39
    # Get the tree (file listing) of a force-pushed commit curl -s "https://api.github.com/repos/OWNER/REPO/git/trees/SHA?recursive=1" | jq .
  • Network accessline 40
    curl -s "https://api.github.com/repos/OWNER/REPO/git/trees/SHA?recursive=1" | jq .
  • Network accessline 42
    # Get a specific file from a force-pushed commit curl -s "https://api.github.com/repos/OWNER/REPO/contents/PATH?ref=SHA" | jq .content | base64 -d
  • Network accessline 43
    curl -s "https://api.github.com/repos/OWNER/REPO/contents/PATH?ref=SHA" | jq .content | base64 -d
  • Network accessline 88
    # Find all archived snapshots of a specific issue curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool
  • Dangerous shell operationline 89
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool
  • Network accessline 89
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUMBER&output=json&limit=50&fl=timestamp,statuscode,original" | python3 -m json.tool
  • Network accessline 91
    # Fetch the best snapshot # Use the timestamp from the CDX result: # https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER
  • Network accessline 92
    # Use the timestamp from the CDX result: # https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER curl -s "https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER" > issue_NUMBER_archived
  • Network accessline 93
    # https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER
  • Network accessline 94
    curl -s "https://web.archive.org/web/TIMESTAMP/https://github.com/OWNER/REPO/issues/NUMBER" > issue_NUMBER_archived.html
  • Network accessline 96
    # Find all snapshots of the repo in a date range curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool
  • Dangerous shell operationline 97
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool
  • Network accessline 97
    curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO*&output=json&from=20240101&to=20240201&limit=200&fl=timestamp,urlkey,statuscode" | python3 -m json.tool
  • Network accessline 103
    # Closed issues (not deleted) are retrievable curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=closed&per_page=100" | jq '.[].number'
  • Network accessline 104
    curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=closed&per_page=100" | jq '.[].number'
  • Network accessline 129
    # Or via API (base64 encoded content): curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c "
  • Dangerous shell operationline 130
    curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c "
  • Network accessline 130
    curl -s "https://api.github.com/repos/OWNER/REPO/contents/path/to/file.py?ref=SHA" | python3 -c "
  • Possible obfuscationline 133
    print(base64.b64decode(d['content']).decode())
  • Network accessline 144
    python3 SKILL_DIR/scripts/evidence-store.py --store evidence.json add    --source "git fetch origin FORCE_PUSHED_SHA"    --content "Recovered commit: FORCE_PUSHED_SHA | Author: attacker@example.com | Date: 2024-01-15 | Added file: malicious
  • Network accessline 147
    --type git \   --actor "attacker-handle" \   --url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \
  • Network accessline 148
    --actor "attacker-handle" \   --url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \   --timestamp "2024-01-15T00:00:00Z" \
  • Network accessline 149
    --url "https://github.com/OWNER/REPO/commit/FORCE_PUSHED_SHA.patch" \

optional-skills/security/oss-forensics/scripts/evidence-store.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 15
    python3 evidence-store.py --store evidence.json add \     --source "git fsck output" --content "dangling commit abc123" \     --type git --actor "malicious-user" --url "https://github.com/owner/repo/commit/abc123"
  • Network accessline 17
    --type git --actor "malicious-user" --url "https://github.com/owner/repo/commit/abc123"
  • Access to secretsline 45
    [Credential-related source omitted]

optional-skills/security/oss-forensics/templates/forensic-report.md

  • Network accessline 149
    - Evidence store: `evidence.json` (SHA-256 integrity: run `python3 SKILL_DIR/scripts/evidence-store.py --store evidence.json verify`) - Related issues: <!-- Link to GitHub issues, CVEs, security advisories --> - RAPTOR framework: https://gi
  • Network accessline 151
    - RAPTOR framework: https://github.com/gadievron/raptor

optional-skills/security/oss-forensics/templates/malicious-package-report.md

  • Access to secretsline 17
    [Credential-related source omitted]

scripts/install.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Network accessline 250
    if ! command -v uv >/dev/null 2>&1; then   printf '%s\n' 'Installing uv (required to bootstrap the Open Gauss installer)...'   curl -LsSf https://astral.sh/uv/install.sh | sh
  • Network accessline 251
    printf '%s\n' 'Installing uv (required to bootstrap the Open Gauss installer)...'   curl -LsSf https://astral.sh/uv/install.sh | sh   export PATH="$HOME/.local/bin:$PATH"
  • Dangerous shell operationline 252
    curl -LsSf https://astral.sh/uv/install.sh | sh
  • Network accessline 252
    curl -LsSf https://astral.sh/uv/install.sh | sh

skills/autonomous-ai-agents/claude-code/SKILL.md

  • Network accessline 15
    Delegate coding tasks to [Claude Code](https://docs.anthropic.com/en/docs/claude-code) via the Gauss terminal. Claude Code is Anthropic's autonomous coding agent CLI.
  • Network accessline 59
    terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && claude 'Review this PR against main. Check for bugs, security issues, and style.'", pty=true)
  • Instruction override signalline 83
    |------|--------| | `claude 'prompt'` | One-shot task, exits when done | | `claude --dangerously-skip-permissions` | Auto-approve all file changes |
  • Instruction override signalline 84
    | `claude 'prompt'` | One-shot task, exits when done | | `claude --dangerously-skip-permissions` | Auto-approve all file changes | | `claude --model <model>` | Use a specific model |
  • Instruction override signalline 85
    | `claude --dangerously-skip-permissions` | Auto-approve all file changes |

skills/autonomous-ai-agents/codex/SKILL.md

  • Network accessline 15
    Delegate coding tasks to [Codex](https://github.com/openai/codex) via the Gauss terminal. Codex is OpenAI's autonomous coding agent CLI.
  • Network accessline 66
    terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && codex review --base origin/main", pty=true)

skills/autonomous-ai-agents/gauss-agent/SKILL.md

  • Network accessline 8
    gauss:     tags: [Agent, Gauss, Multi-Agent, Orchestration, Subprocess, Interactive]     homepage: https://github.com/NousResearch/gauss-agent
  • Network accessline 9
    tags: [Agent, Gauss, Multi-Agent, Orchestration, Subprocess, Interactive]     homepage: https://github.com/NousResearch/gauss-agent     related_skills: [claude-code, codex]
  • Network accessline 10
    homepage: https://github.com/NousResearch/gauss-agent
  • Dangerous shell operationline 40
    curl -fsSL https://raw.githubusercontent.com/NousResearch/gauss-agent/main/scripts/install.sh | bash
  • Network accessline 40
    curl -fsSL https://raw.githubusercontent.com/NousResearch/gauss-agent/main/scripts/install.sh | bash
  • Network accessline 43
    This installs uv, Python 3.11, clones the repo, sets up the venv, and launches an interactive setup wizard to configure your API provider and model. See the [GitHub repo](https://github.com/NousResearch/gauss-agent) for details.

skills/autonomous-ai-agents/opencode/SKILL.md

  • Network accessline 15
    Use [OpenCode](https://opencode.ai) as an autonomous coding worker orchestrated by Gauss terminal/process tools. OpenCode is a provider-agnostic, open-source AI coding agent with a TUI and CLI.
  • Access to secretsline 27
    [Credential-related source omitted]
  • Network accessline 160
    terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && opencode run 'Review this PR vs main. Report bugs, security risks, test gaps, and style issues.' -f $(git diff origin/main --name-o

skills/creative/ascii-art/SKILL.md

  • Network accessline 63
    # Basic text banner (default font) curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World"
  • Network accessline 64
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World"
  • Network accessline 66
    # With a specific font curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant"
  • Network accessline 67
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant"
  • Network accessline 68
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Doom"
  • Network accessline 69
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Star+Wars"
  • Network accessline 70
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=3-D"
  • Network accessline 71
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Banner3"
  • Network accessline 73
    # List all available fonts (returns JSON array) curl -s "https://asciified.thelicato.io/api/v2/fonts"
  • Network accessline 74
    curl -s "https://asciified.thelicato.io/api/v2/fonts"
  • Network accessline 80
    - The response is plain text ASCII art — no JSON wrapping, ready to display - Font names are case-sensitive; use the fonts endpoint to get exact names - Works from any terminal with curl — no Python or pip needed
  • Network accessline 82
    - Works from any terminal with curl — no Python or pip needed
  • Dangerous shell operationline 91
    sudo apt install cowsay -y    # Debian/Ubuntu
  • Dangerous shell operationline 135
    sudo apt install boxes -y    # Debian/Ubuntu
  • Network accessline 158
    python3 -m pyfiglet "GAUSS" -f slant | boxes -d stone # Or without pyfiglet installed: curl -s "https://asciified.thelicato.io/api/v2/ascii?text=GAUSS&font=Slant" | boxes -d stone
  • Network accessline 160
    curl -s "https://asciified.thelicato.io/api/v2/ascii?text=GAUSS&font=Slant" | boxes -d stone
  • Dangerous shell operationline 170
    sudo apt install toilet toilet-fonts -y    # Debian/Ubuntu
  • Dangerous shell operationline 201
    sudo snap install ascii-image-converter
  • Network accessline 209
    ascii-image-converter image.png -b               # Braille characters ascii-image-converter image.png -n               # Negative/inverted ascii-image-converter https://url/image.jpg      # Direct URL
  • Network accessline 210
    ascii-image-converter image.png -n               # Negative/inverted ascii-image-converter https://url/image.jpg      # Direct URL ascii-image-converter image.png --save-txt out   # Save as text
  • Network accessline 211
    ascii-image-converter https://url/image.jpg      # Direct URL
  • Dangerous shell operationline 218
    sudo apt install jp2a -y
  • Network accessline 231
    **URL pattern:** `https://ascii.co.uk/art/{subject}`
  • Network accessline 236
    curl -s 'https://ascii.co.uk/art/cat' -o /tmp/ascii_art.html
  • Network accessline 271
    curl -s https://api.github.com/octocat
  • Network accessline 281
    curl -s "qrenco.de/Hello+World"
  • Network accessline 282
    curl -s "qrenco.de/https://example.com"
  • Network accessline 288
    curl -s "wttr.in/London"          # Full weather report with ASCII graphics
  • Network accessline 289
    curl -s "wttr.in/Moon"            # Moon phase in ASCII art
  • Network accessline 290
    curl -s "v2.wttr.in/London"       # Detailed version
  • Network accessline 313
    1. **Text as a banner** → pyfiglet if installed, otherwise asciified API via curl 2. **Wrap a message in fun character art** → cowsay
  • Network accessline 314
    2. **Wrap a message in fun character art** → cowsay 3. **Add decorative border/frame** → boxes (can combine with pyfiglet/asciified) 4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing
  • Network accessline 315
    3. **Add decorative border/frame** → boxes (can combine with pyfiglet/asciified) 4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing 5. **Convert an image to ASCII** → ascii-image-converter or jp2a
  • Network accessline 316
    4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing
  • Network accessline 317
    5. **Convert an image to ASCII** → ascii-image-converter or jp2a 6. **QR code** → qrenco.de via curl 7. **Weather/moon art** → wttr.in via curl
  • Network accessline 318
    6. **QR code** → qrenco.de via curl 7. **Weather/moon art** → wttr.in via curl 8. **Something custom/creative** → LLM generation with Unicode palette
  • Network accessline 319
    7. **Weather/moon art** → wttr.in via curl 8. **Something custom/creative** → LLM generation with Unicode palette 9. **Any tool not installed** → install it, or fall back to next option

skills/creative/ascii-video/README.md

  • Network accessline 5
    Built for [Gauss Agent](https://github.com/NousResearch/gauss-agent). Usable in any coding agent. Canonical source lives here; synced to [`NousResearch/gauss-agent/skills/creative/ascii-video`](https://github.com/NousResearch/gauss-agent/tr

skills/creative/ascii-video/references/inputs.md

  • Access to secretsline 308
    [Credential-related source omitted]
  • Network accessline 314
    url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
  • Access to secretsline 315
    [Credential-related source omitted]
  • Network accessline 324
    },     }     resp = requests.post(url, json=data, headers=headers, stream=True)
  • Network accessline 325
    }     resp = requests.post(url, json=data, headers=headers, stream=True)     resp.raise_for_status()
  • Network accessline 326
    resp = requests.post(url, json=data, headers=headers, stream=True)

skills/creative/ascii-video/references/shaders.md

  • Access to secretsline 1350
    [Credential-related source omitted]
  • Access to secretsline 1353
    [Credential-related source omitted]

skills/creative/excalidraw/SKILL.md

  • Network accessline 17
    Create diagrams by writing standard Excalidraw element JSON and saving as `.excalidraw` files. These files can be drag-and-dropped onto [excalidraw.com](https://excalidraw.com) for viewing and editing. No accounts, no API keys, no rendering

skills/creative/excalidraw/scripts/upload.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 15
    Example:     python upload.py ~/diagrams/architecture.excalidraw     # prints: https://excalidraw.com/#json=abc123,encryptionKeyHere
  • Network accessline 16
    python upload.py ~/diagrams/architecture.excalidraw     # prints: https://excalidraw.com/#json=abc123,encryptionKeyHere """
  • Network accessline 17
    # prints: https://excalidraw.com/#json=abc123,encryptionKeyHere
  • Network accessline 36
    UPLOAD_URL = "https://json.excalidraw.com/api/v2/post/"
  • Network accessline 88
    req = urllib.request.Request(UPLOAD_URL, data=payload, method="POST")
  • Network accessline 89
    with urllib.request.urlopen(req, timeout=30) as resp:
  • Network accessline 101
    return f"https://excalidraw.com/#json={file_id},{key_b64}"

skills/data-science/jupyter-live-kernel/SKILL.md

  • Network accessline 49
    git clone https://github.com/hamelsmu/hamelnb.git ~/.agent-skills/hamelnb
  • Network accessline 77
    curl -s -X POST http://127.0.0.1:8888/api/sessions \

skills/dogfood/SKILL.md

  • Network accessline 55
    browser_navigate(url="https://example.com/page")

skills/email/himalaya/SKILL.md

  • Network accessline 8
    gauss:     tags: [Email, IMAP, SMTP, CLI, Communication]     homepage: https://github.com/pimalaya/himalaya
  • Network accessline 9
    tags: [Email, IMAP, SMTP, CLI, Communication]     homepage: https://github.com/pimalaya/himalaya prerequisites:
  • Network accessline 10
    homepage: https://github.com/pimalaya/himalaya
  • Network accessline 33
    # Pre-built binary (Linux/macOS — recommended) curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh
  • Network accessline 34
    curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh

skills/email/himalaya/references/configuration.md

  • Access to secretsline 44
    [Credential-related source omitted]
  • Network accessline 155
    backend.auth.access-token.cmd = "pass show oauth/access-token" backend.auth.refresh-token.cmd = "pass show oauth/refresh-token" backend.auth.auth-url = "https://provider.com/oauth/authorize"
  • Network accessline 156
    backend.auth.refresh-token.cmd = "pass show oauth/refresh-token" backend.auth.auth-url = "https://provider.com/oauth/authorize" backend.auth.token-url = "https://provider.com/oauth/token"
  • Network accessline 157
    backend.auth.auth-url = "https://provider.com/oauth/authorize"
  • Network accessline 158
    backend.auth.token-url = "https://provider.com/oauth/token"

skills/gaming/minecraft-modpack-server/SKILL.md

  • Network accessline 34
    mkdir -p ~/minecraft-server cd ~/minecraft-server wget -O serverpack.zip "<URL>"
  • Network accessline 35
    cd ~/minecraft-server wget -O serverpack.zip "<URL>" unzip -o serverpack.zip -d server
  • Network accessline 36
    wget -O serverpack.zip "<URL>"
  • Dangerous shell operationline 44
    - Minecraft 1.21+ → Java 21: `sudo apt install openjdk-21-jre-headless`
  • Dangerous shell operationline 45
    - Minecraft 1.18-1.20 → Java 17: `sudo apt install openjdk-17-jre-headless`
  • Dangerous shell operationline 46
    - Minecraft 1.16 and below → Java 8: `sudo apt install openjdk-8-jre-headless`
  • Dangerous shell operationline 124
    sudo ufw allow 25565/tcp comment "Minecraft Server"
  • Dangerous shell operationline 126
    Check with: `sudo ufw status | grep 25565`
  • Dangerous shell operationline 159
    ls -1t "$BACKUP_DIR"/world_*.tar.gz | tail -n "$REMOVE" | xargs rm -f

skills/github/github-auth/SKILL.md

  • Dangerous shell operationline 37
    3. If `gh` is not installed → use "git-only" method below (no sudo needed)
  • Network accessline 51
    Tell the user to go to: **https://github.com/settings/tokens**
  • Access to secretsline 66
    [Credential-related source omitted]
  • Network accessline 70
    [Credential-related source omitted]
  • Network accessline 72
    git ls-remote https://github.com/<their-username>/<any-repo>.git
  • Network accessline 102
    # Test push access (this should work without any prompts now) git ls-remote https://github.com/<their-username>/<any-repo>.git
  • Network accessline 103
    git ls-remote https://github.com/<their-username>/<any-repo>.git
  • Access to secretsline 117
    [Credential-related source omitted]
  • Access to secretsline 124
    [Credential-related source omitted]
  • Access to secretsline 127
    [Credential-related source omitted]
  • Network accessline 130
    Tell the user to add the public key at: **https://github.com/settings/keys**
  • Network accessline 145
    # Rewrite HTTPS GitHub URLs to SSH automatically git config --global url."git@github.com:".insteadOf "https://github.com/"
  • Network accessline 146
    git config --global url."git@github.com:".insteadOf "https://github.com/"
  • Access to secretsline 196
    [Credential-related source omitted]
  • Network accessline 198
    # Then use in curl calls:
  • Network accessline 199
    [Credential-related source omitted]
  • Access to secretsline 199
    [Credential-related source omitted]
  • Network accessline 200
    https://api.github.com/user
  • Network accessline 208
    # Read from git credential store grep "github.com" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\([^@]*\)@.*|\1|'
  • Network accessline 209
    [Credential-related source omitted]
  • Access to secretsline 209
    [Credential-related source omitted]
  • Network accessline 217
    # Try gh first, fall back to git + curl if command -v gh &>/dev/null && gh auth status &>/dev/null; then
  • Access to secretsline 220
    [Credential-related source omitted]
  • Network accessline 221
    [Credential-related source omitted]
  • Network accessline 222
    [Credential-related source omitted]
  • Access to secretsline 222
    [Credential-related source omitted]
  • Network accessline 223
    [Credential-related source omitted]
  • Access to secretsline 223
    [Credential-related source omitted]
  • Access to secretsline 240
    [Credential-related source omitted]
  • Access to secretsline 242
    [Credential-related source omitted]
  • Dangerous shell operationline 243
    | `gh: command not found` + no sudo | Use git-only Method 1 above — no installation needed |

skills/github/github-auth/scripts/gh-env.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Access to secretsline 18
    [Credential-related source omitted]
  • Network accessline 24
    [Credential-related source omitted]
  • Access to secretsline 24
    [Credential-related source omitted]
  • Network accessline 25
    [Credential-related source omitted]
  • Network accessline 26
    [Credential-related source omitted]
  • Access to secretsline 26
    [Credential-related source omitted]
  • Network accessline 27
    [Credential-related source omitted]
  • Access to secretsline 27
    [Credential-related source omitted]
  • Network accessline 28
    [Credential-related source omitted]
  • Access to secretsline 28
    [Credential-related source omitted]
  • Network accessline 29
    GH_AUTH_METHOD="curl"     fi fi
  • Network accessline 33
    # Resolve username for curl method
  • Network accessline 34
    if [ "$GH_AUTH_METHOD" = "curl" ] && [ -z "$GH_USER" ]; then
  • Dangerous shell operationline 35
    [Credential-related source omitted]
  • Network accessline 35
    [Credential-related source omitted]
  • Access to secretsline 35
    [Credential-related source omitted]
  • Network accessline 36
    https://api.github.com/user 2>/dev/null \
  • Network accessline 56
    echo "GitHub Auth: $GH_AUTH_METHOD" [ -n "$GH_USER" ]       && echo "User: $GH_USER"
  • Network accessline 57
    [ -n "$GH_USER" ]       && echo "User: $GH_USER" [ -n "$GH_OWNER_REPO" ] && echo "Repo: $GH_OWNER_REPO" [ "$GH_AUTH_METHOD" = "none" ] && echo "⚠ Not authenticated — see github-auth skill"
  • Network accessline 59
    [ "$GH_AUTH_METHOD" = "none" ] && echo "⚠ Not authenticated — see github-auth skill"

skills/github/github-code-review/SKILL.md

  • Network accessline 28
    [Credential-related source omitted]
  • Network accessline 29
    [Credential-related source omitted]
  • Access to secretsline 29
    [Credential-related source omitted]
  • Network accessline 30
    [Credential-related source omitted]
  • Access to secretsline 30
    [Credential-related source omitted]
  • Access to secretsline 87
    [Credential-related source omitted]
  • Network accessline 138
    # Get PR details curl -s \
  • Dangerous shell operationline 139
    [Credential-related source omitted]
  • Network accessline 139
    [Credential-related source omitted]
  • Access to secretsline 139
    [Credential-related source omitted]
  • Network accessline 140
    [Credential-related source omitted]
  • Access to secretsline 140
    [Credential-related source omitted]
  • Network accessline 141
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \
  • Network accessline 151
    # List changed files curl -s \
  • Dangerous shell operationline 152
    [Credential-related source omitted]
  • Network accessline 152
    [Credential-related source omitted]
  • Access to secretsline 152
    [Credential-related source omitted]
  • Network accessline 153
    [Credential-related source omitted]
  • Access to secretsline 153
    [Credential-related source omitted]
  • Network accessline 154
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/files \
  • Network accessline 193
    [Credential-related source omitted]
  • Access to secretsline 193
    [Credential-related source omitted]
  • Network accessline 194
    [Credential-related source omitted]
  • Access to secretsline 194
    [Credential-related source omitted]
  • Network accessline 195
    https://api.github.com/repos/$OWNER/$REPO/issues/$PR_NUMBER/comments \
  • Network accessline 218
    # Get the head commit SHA HEAD_SHA=$(curl -s \
  • Dangerous shell operationline 219
    [Credential-related source omitted]
  • Network accessline 219
    [Credential-related source omitted]
  • Access to secretsline 219
    [Credential-related source omitted]
  • Network accessline 220
    [Credential-related source omitted]
  • Access to secretsline 220
    [Credential-related source omitted]
  • Network accessline 221
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \
  • Network accessline 224
    [Credential-related source omitted]
  • Access to secretsline 224
    [Credential-related source omitted]
  • Network accessline 225
    [Credential-related source omitted]
  • Access to secretsline 225
    [Credential-related source omitted]
  • Network accessline 226
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/comments \
  • Network accessline 246
    **With curl — multi-comment review submitted atomically:**
  • Dangerous shell operationline 249
    [Credential-related source omitted]
  • Network accessline 249
    [Credential-related source omitted]
  • Access to secretsline 249
    [Credential-related source omitted]
  • Network accessline 250
    [Credential-related source omitted]
  • Access to secretsline 250
    [Credential-related source omitted]
  • Network accessline 251
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \
  • Network accessline 254
    [Credential-related source omitted]
  • Access to secretsline 254
    [Credential-related source omitted]
  • Network accessline 255
    [Credential-related source omitted]
  • Access to secretsline 255
    [Credential-related source omitted]
  • Network accessline 256
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/reviews \
  • Network accessline 352
    [Credential-related source omitted]
  • Network accessline 353
    [Credential-related source omitted]
  • Access to secretsline 353
    [Credential-related source omitted]
  • Network accessline 354
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER
  • Network accessline 356
    [Credential-related source omitted]
  • Network accessline 357
    [Credential-related source omitted]
  • Access to secretsline 357
    [Credential-related source omitted]
  • Network accessline 358
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/files
  • Network accessline 413
    **With curl — atomic review with multiple inline comments:**
  • Dangerous shell operationline 415
    [Credential-related source omitted]
  • Network accessline 415
    [Credential-related source omitted]
  • Access to secretsline 415
    [Credential-related source omitted]
  • Network accessline 416
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER \
  • Network accessline 419
    # Build the review JSON — event is APPROVE, REQUEST_CHANGES, or COMMENT curl -s -X POST \
  • Network accessline 420
    [Credential-related source omitted]
  • Access to secretsline 420
    [Credential-related source omitted]
  • Network accessline 421
    [Credential-related source omitted]
  • Access to secretsline 421
    [Credential-related source omitted]
  • Network accessline 422
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/reviews \

skills/github/github-issues/SKILL.md

  • Network accessline 28
    [Credential-related source omitted]
  • Network accessline 29
    [Credential-related source omitted]
  • Access to secretsline 29
    [Credential-related source omitted]
  • Network accessline 30
    [Credential-related source omitted]
  • Access to secretsline 30
    [Credential-related source omitted]
  • Network accessline 57
    # List open issues curl -s \
  • Dangerous shell operationline 58
    [Credential-related source omitted]
  • Network accessline 58
    [Credential-related source omitted]
  • Access to secretsline 58
    [Credential-related source omitted]
  • Network accessline 59
    [Credential-related source omitted]
  • Access to secretsline 59
    [Credential-related source omitted]
  • Network accessline 60
    "https://api.github.com/repos/$OWNER/$REPO/issues?state=open&per_page=20" \
  • Network accessline 68
    # Filter by label curl -s \
  • Dangerous shell operationline 69
    [Credential-related source omitted]
  • Network accessline 69
    [Credential-related source omitted]
  • Access to secretsline 69
    [Credential-related source omitted]
  • Network accessline 70
    [Credential-related source omitted]
  • Access to secretsline 70
    [Credential-related source omitted]
  • Network accessline 71
    "https://api.github.com/repos/$OWNER/$REPO/issues?state=open&labels=bug&per_page=20" \
  • Network accessline 78
    # View a specific issue curl -s \
  • Dangerous shell operationline 79
    [Credential-related source omitted]
  • Network accessline 79
    [Credential-related source omitted]
  • Access to secretsline 79
    [Credential-related source omitted]
  • Network accessline 80
    [Credential-related source omitted]
  • Access to secretsline 80
    [Credential-related source omitted]
  • Network accessline 81
    https://api.github.com/repos/$OWNER/$REPO/issues/42 \
  • Network accessline 92
    # Search issues curl -s \
  • Dangerous shell operationline 93
    [Credential-related source omitted]
  • Network accessline 93
    [Credential-related source omitted]
  • Access to secretsline 93
    [Credential-related source omitted]
  • Network accessline 94
    [Credential-related source omitted]
  • Access to secretsline 94
    [Credential-related source omitted]
  • Network accessline 95
    "https://api.github.com/search/issues?q=authentication+error+repo:$OWNER/$REPO" \
  • Network accessline 127
    [Credential-related source omitted]
  • Access to secretsline 127
    [Credential-related source omitted]
  • Network accessline 128
    [Credential-related source omitted]
  • Access to secretsline 128
    [Credential-related source omitted]
  • Network accessline 129
    https://api.github.com/repos/$OWNER/$REPO/issues \
  • Network accessline 189
    # Add labels curl -s -X POST \
  • Network accessline 190
    [Credential-related source omitted]
  • Access to secretsline 190
    [Credential-related source omitted]
  • Network accessline 191
    [Credential-related source omitted]
  • Access to secretsline 191
    [Credential-related source omitted]
  • Network accessline 192
    https://api.github.com/repos/$OWNER/$REPO/issues/42/labels \
  • Network accessline 195
    # Remove a label curl -s -X DELETE \
  • Network accessline 196
    [Credential-related source omitted]
  • Access to secretsline 196
    [Credential-related source omitted]
  • Access to secretsline 197
    [Credential-related source omitted]
  • Network accessline 198
    https://api.github.com/repos/$OWNER/$REPO/issues/42/labels/needs-triage
  • Network accessline 200
    # List available labels in the repo curl -s \
  • Dangerous shell operationline 201
    [Credential-related source omitted]
  • Network accessline 201
    [Credential-related source omitted]
  • Access to secretsline 201
    [Credential-related source omitted]
  • Network accessline 202
    [Credential-related source omitted]
  • Access to secretsline 202
    [Credential-related source omitted]
  • Network accessline 203
    https://api.github.com/repos/$OWNER/$REPO/labels \
  • Network accessline 222
    [Credential-related source omitted]
  • Access to secretsline 222
    [Credential-related source omitted]
  • Network accessline 223
    [Credential-related source omitted]
  • Access to secretsline 223
    [Credential-related source omitted]
  • Network accessline 224
    https://api.github.com/repos/$OWNER/$REPO/issues/42/assignees \
  • Network accessline 239
    [Credential-related source omitted]
  • Access to secretsline 239
    [Credential-related source omitted]
  • Network accessline 240
    [Credential-related source omitted]
  • Access to secretsline 240
    [Credential-related source omitted]
  • Network accessline 241
    https://api.github.com/repos/$OWNER/$REPO/issues/42/comments \
  • Network accessline 258
    # Close curl -s -X PATCH \
  • Network accessline 259
    [Credential-related source omitted]
  • Access to secretsline 259
    [Credential-related source omitted]
  • Network accessline 260
    [Credential-related source omitted]
  • Access to secretsline 260
    [Credential-related source omitted]
  • Network accessline 261
    https://api.github.com/repos/$OWNER/$REPO/issues/42 \
  • Network accessline 264
    # Reopen curl -s -X PATCH \
  • Network accessline 265
    [Credential-related source omitted]
  • Access to secretsline 265
    [Credential-related source omitted]
  • Network accessline 266
    [Credential-related source omitted]
  • Access to secretsline 266
    [Credential-related source omitted]
  • Network accessline 267
    https://api.github.com/repos/$OWNER/$REPO/issues/42 \
  • Network accessline 306
    # With curl curl -s \
  • Dangerous shell operationline 307
    [Credential-related source omitted]
  • Network accessline 307
    [Credential-related source omitted]
  • Access to secretsline 307
    [Credential-related source omitted]
  • Network accessline 308
    [Credential-related source omitted]
  • Access to secretsline 308
    [Credential-related source omitted]
  • Network accessline 309
    "https://api.github.com/repos/$OWNER/$REPO/issues?labels=needs-triage&state=open" \
  • Network accessline 340
    # List issue numbers with a label, then close each curl -s \
  • Dangerous shell operationline 341
    [Credential-related source omitted]
  • Network accessline 341
    [Credential-related source omitted]
  • Access to secretsline 341
    [Credential-related source omitted]
  • Network accessline 342
    [Credential-related source omitted]
  • Access to secretsline 342
    [Credential-related source omitted]
  • Network accessline 343
    "https://api.github.com/repos/$OWNER/$REPO/issues?labels=wontfix&state=open" \
  • Network accessline 344
    | python3 -c "import sys,json; [print(i['number']) for i in json.load(sys.stdin)]" \   | while read num; do     curl -s -X PATCH \
  • Network accessline 345
    [Credential-related source omitted]
  • Network accessline 346
    [Credential-related source omitted]
  • Access to secretsline 346
    [Credential-related source omitted]
  • Access to secretsline 347
    [Credential-related source omitted]
  • Network accessline 348
    https://api.github.com/repos/$OWNER/$REPO/issues/$num \
  • Network accessline 356
    | Action | gh | curl endpoint |

skills/github/github-pr-workflow/SKILL.md

  • Network accessline 30
    [Credential-related source omitted]
  • Network accessline 31
    [Credential-related source omitted]
  • Access to secretsline 31
    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Access to secretsline 32
    [Credential-related source omitted]
  • Network accessline 131
    [Credential-related source omitted]
  • Access to secretsline 131
    [Credential-related source omitted]
  • Network accessline 132
    [Credential-related source omitted]
  • Access to secretsline 132
    [Credential-related source omitted]
  • Network accessline 133
    -H "Accept: application/vnd.github.v3+json" \   https://api.github.com/repos/$OWNER/$REPO/pulls \   -d "{
  • Network accessline 134
    https://api.github.com/repos/$OWNER/$REPO/pulls \
  • Network accessline 167
    # Query the combined status curl -s \
  • Dangerous shell operationline 168
    [Credential-related source omitted]
  • Network accessline 168
    [Credential-related source omitted]
  • Access to secretsline 168
    [Credential-related source omitted]
  • Network accessline 169
    [Credential-related source omitted]
  • Access to secretsline 169
    [Credential-related source omitted]
  • Network accessline 170
    https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \
  • Network accessline 178
    # Also check GitHub Actions check runs (separate endpoint) curl -s \
  • Dangerous shell operationline 179
    [Credential-related source omitted]
  • Network accessline 179
    [Credential-related source omitted]
  • Access to secretsline 179
    [Credential-related source omitted]
  • Network accessline 180
    [Credential-related source omitted]
  • Access to secretsline 180
    [Credential-related source omitted]
  • Network accessline 181
    https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \
  • Network accessline 193
    SHA=$(git rev-parse HEAD) for i in $(seq 1 20); do   STATUS=$(curl -s \
  • Network accessline 194
    [Credential-related source omitted]
  • Dangerous shell operationline 195
    [Credential-related source omitted]
  • Network accessline 195
    [Credential-related source omitted]
  • Access to secretsline 195
    [Credential-related source omitted]
  • Access to secretsline 196
    [Credential-related source omitted]
  • Network accessline 197
    https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \
  • Network accessline 228
    # List workflow runs on this branch curl -s \
  • Dangerous shell operationline 229
    [Credential-related source omitted]
  • Network accessline 229
    [Credential-related source omitted]
  • Access to secretsline 229
    [Credential-related source omitted]
  • Network accessline 230
    [Credential-related source omitted]
  • Access to secretsline 230
    [Credential-related source omitted]
  • Network accessline 231
    "https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5" \
  • Network accessline 238
    # Get failed job logs (download as zip, extract, read) RUN_ID=<run_id> curl -s -L \
  • Network accessline 239
    [Credential-related source omitted]
  • Network accessline 240
    [Credential-related source omitted]
  • Access to secretsline 240
    [Credential-related source omitted]
  • Network accessline 241
    [Credential-related source omitted]
  • Access to secretsline 241
    [Credential-related source omitted]
  • Network accessline 242
    https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \
  • Network accessline 289
    # Merge the PR via API (squash) curl -s -X PUT \
  • Network accessline 290
    [Credential-related source omitted]
  • Access to secretsline 290
    [Credential-related source omitted]
  • Network accessline 291
    [Credential-related source omitted]
  • Access to secretsline 291
    [Credential-related source omitted]
  • Network accessline 292
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \
  • Network accessline 312
    # Auto-merge requires the repo to have it enabled in settings. # This uses the GraphQL API since REST doesn't support auto-merge. PR_NODE_ID=$(curl -s \
  • Network accessline 313
    [Credential-related source omitted]
  • Dangerous shell operationline 314
    [Credential-related source omitted]
  • Network accessline 314
    [Credential-related source omitted]
  • Access to secretsline 314
    [Credential-related source omitted]
  • Network accessline 315
    [Credential-related source omitted]
  • Access to secretsline 315
    [Credential-related source omitted]
  • Network accessline 316
    https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \
  • Network accessline 319
    [Credential-related source omitted]
  • Access to secretsline 319
    [Credential-related source omitted]
  • Network accessline 320
    [Credential-related source omitted]
  • Access to secretsline 320
    [Credential-related source omitted]
  • Network accessline 321
    https://api.github.com/graphql \
  • Network accessline 345
    # 6. Create PR (picks gh or curl based on what's available)
  • Network accessline 355
    | Action | gh | git + curl |
  • Network accessline 356
    [Credential-related source omitted]
  • Network accessline 357
    [Credential-related source omitted]
  • Access to secretsline 357
    [Credential-related source omitted]
  • Network accessline 358
    | View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H "Accept: application/vnd.github.diff" ...` |
  • Network accessline 359
    | Add comment | `gh pr comment N --body "..."` | `curl -X POST .../issues/N/comments -d '{"body":"..."}'` |
  • Network accessline 360
    | Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{"reviewers":["user"]}'` |
  • Network accessline 361
    | Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{"state":"closed"}'` |

skills/github/github-pr-workflow/references/ci-troubleshooting.md

  • Network accessline 11
    # With curl — download and extract
  • Network accessline 12
    [Credential-related source omitted]
  • Access to secretsline 12
    [Credential-related source omitted]
  • Network accessline 13
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/actions/runs/<RUN_ID>/logs \
  • Network accessline 104
    fatal: could not read Username for 'https://github.com': No such device or address
  • Network accessline 180
    [Credential-related source omitted]
  • Network accessline 181
    [Credential-related source omitted]
  • Network accessline 182
    [Credential-related source omitted]

skills/github/github-pr-workflow/references/conventional-commits.md

  • Network accessline 32
    BREAKING CHANGE: API endpoints now require Bearer token instead of API key header. Migration guide: https://docs.example.com/migrate-auth
  • Network accessline 33
    Migration guide: https://docs.example.com/migrate-auth

skills/github/github-repo-management/SKILL.md

  • Network accessline 27
    [Credential-related source omitted]
  • Network accessline 28
    [Credential-related source omitted]
  • Access to secretsline 28
    [Credential-related source omitted]
  • Network accessline 29
    [Credential-related source omitted]
  • Access to secretsline 29
    [Credential-related source omitted]
  • Network accessline 35
    [Credential-related source omitted]
  • Network accessline 36
    [Credential-related source omitted]
  • Dangerous shell operationline 37
    [Credential-related source omitted]
  • Network accessline 37
    [Credential-related source omitted]
  • Access to secretsline 37
    [Credential-related source omitted]
  • Network accessline 57
    # Clone via HTTPS (works with credential helper or token-embedded URL) git clone https://github.com/owner/repo-name.git
  • Network accessline 58
    git clone https://github.com/owner/repo-name.git
  • Network accessline 60
    # Clone into a specific directory git clone https://github.com/owner/repo-name.git ./my-local-dir
  • Network accessline 61
    git clone https://github.com/owner/repo-name.git ./my-local-dir
  • Network accessline 63
    # Shallow clone (faster for large repos) git clone --depth 1 https://github.com/owner/repo-name.git
  • Network accessline 64
    git clone --depth 1 https://github.com/owner/repo-name.git
  • Network accessline 66
    # Clone a specific branch git clone --branch develop https://github.com/owner/repo-name.git
  • Network accessline 67
    git clone --branch develop https://github.com/owner/repo-name.git
  • Network accessline 102
    # Create the remote repo via API curl -s -X POST \
  • Network accessline 103
    [Credential-related source omitted]
  • Access to secretsline 103
    [Credential-related source omitted]
  • Network accessline 104
    [Credential-related source omitted]
  • Access to secretsline 104
    [Credential-related source omitted]
  • Network accessline 105
    https://api.github.com/user/repos \
  • Network accessline 114
    # Clone it git clone https://github.com/$GH_USER/my-new-project.git
  • Network accessline 115
    git clone https://github.com/$GH_USER/my-new-project.git
  • Network accessline 121
    git add . git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git
  • Network accessline 122
    git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git git push -u origin main
  • Network accessline 123
    git remote add origin https://github.com/$GH_USER/my-new-project.git
  • Network accessline 130
    [Credential-related source omitted]
  • Access to secretsline 130
    [Credential-related source omitted]
  • Network accessline 131
    [Credential-related source omitted]
  • Access to secretsline 131
    [Credential-related source omitted]
  • Network accessline 132
    https://api.github.com/orgs/my-org/repos \
  • Network accessline 147
    [Credential-related source omitted]
  • Access to secretsline 147
    [Credential-related source omitted]
  • Network accessline 148
    [Credential-related source omitted]
  • Access to secretsline 148
    [Credential-related source omitted]
  • Network accessline 149
    https://api.github.com/repos/owner/template-repo/generate \
  • Network accessline 164
    # Create the fork via API curl -s -X POST \
  • Network accessline 165
    [Credential-related source omitted]
  • Access to secretsline 165
    [Credential-related source omitted]
  • Access to secretsline 166
    [Credential-related source omitted]
  • Network accessline 167
    https://api.github.com/repos/owner/repo-name/forks
  • Network accessline 169
    # Wait a moment for GitHub to create it, then clone sleep 3 git clone https://github.com/$GH_USER/repo-name.git
  • Network accessline 170
    sleep 3 git clone https://github.com/$GH_USER/repo-name.git cd repo-name
  • Network accessline 171
    git clone https://github.com/$GH_USER/repo-name.git
  • Network accessline 174
    # Add the original repo as "upstream" remote git remote add upstream https://github.com/owner/repo-name.git
  • Network accessline 175
    git remote add upstream https://github.com/owner/repo-name.git
  • Network accessline 207
    # View repo details curl -s \
  • Dangerous shell operationline 208
    [Credential-related source omitted]
  • Network accessline 208
    [Credential-related source omitted]
  • Access to secretsline 208
    [Credential-related source omitted]
  • Network accessline 209
    [Credential-related source omitted]
  • Access to secretsline 209
    [Credential-related source omitted]
  • Network accessline 210
    https://api.github.com/repos/$OWNER/$REPO \
  • Network accessline 220
    # List your repos curl -s \
  • Dangerous shell operationline 221
    [Credential-related source omitted]
  • Network accessline 221
    [Credential-related source omitted]
  • Access to secretsline 221
    [Credential-related source omitted]
  • Network accessline 222
    [Credential-related source omitted]
  • Access to secretsline 222
    [Credential-related source omitted]
  • Network accessline 223
    "https://api.github.com/user/repos?per_page=20&sort=updated" \
  • Network accessline 230
    # Search repos curl -s \
  • Dangerous shell operationline 231
    curl -s    "https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10"    | python3 -c "
  • Network accessline 231
    curl -s \
  • Network accessline 232
    "https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10" \
  • Network accessline 254
    [Credential-related source omitted]
  • Access to secretsline 254
    [Credential-related source omitted]
  • Network accessline 255
    [Credential-related source omitted]
  • Access to secretsline 255
    [Credential-related source omitted]
  • Network accessline 256
    https://api.github.com/repos/$OWNER/$REPO \
  • Network accessline 264
    # Update topics curl -s -X PUT \
  • Network accessline 265
    [Credential-related source omitted]
  • Access to secretsline 265
    [Credential-related source omitted]
  • Network accessline 266
    [Credential-related source omitted]
  • Access to secretsline 266
    [Credential-related source omitted]
  • Network accessline 267
    -H "Accept: application/vnd.github.mercy-preview+json" \   https://api.github.com/repos/$OWNER/$REPO/topics \   -d '{"names": ["machine-learning", "python", "automation"]}'
  • Network accessline 268
    https://api.github.com/repos/$OWNER/$REPO/topics \
  • Network accessline 275
    # View current protection curl -s \
  • Network accessline 276
    [Credential-related source omitted]
  • Access to secretsline 276
    [Credential-related source omitted]
  • Access to secretsline 277
    [Credential-related source omitted]
  • Network accessline 278
    https://api.github.com/repos/$OWNER/$REPO/branches/main/protection
  • Network accessline 280
    # Set up branch protection curl -s -X PUT \
  • Network accessline 281
    [Credential-related source omitted]
  • Access to secretsline 281
    [Credential-related source omitted]
  • Network accessline 282
    [Credential-related source omitted]
  • Access to secretsline 282
    [Credential-related source omitted]
  • Network accessline 283
    https://api.github.com/repos/$OWNER/$REPO/branches/main/protection \
  • Access to secretsline 302
    [Credential-related source omitted]
  • Access to secretsline 303
    [Credential-related source omitted]
  • Access to secretsline 305
    [Credential-related source omitted]
  • Network accessline 313
    # Get the repo's public key for encrypting secrets curl -s \
  • Network accessline 314
    [Credential-related source omitted]
  • Access to secretsline 314
    [Credential-related source omitted]
  • Access to secretsline 315
    [Credential-related source omitted]
  • Network accessline 316
    https://api.github.com/repos/$OWNER/$REPO/actions/secrets/public-key
  • Network accessline 337
    # Then PUT the encrypted secret curl -s -X PUT \
  • Network accessline 338
    [Credential-related source omitted]
  • Access to secretsline 338
    [Credential-related source omitted]
  • Network accessline 339
    [Credential-related source omitted]
  • Access to secretsline 339
    [Credential-related source omitted]
  • Network accessline 340
    [Credential-related source omitted]
  • Access to secretsline 340
    [Credential-related source omitted]
  • Network accessline 343
    # List secrets (names only, values hidden) curl -s \
  • Dangerous shell operationline 344
    [Credential-related source omitted]
  • Network accessline 344
    [Credential-related source omitted]
  • Access to secretsline 344
    [Credential-related source omitted]
  • Network accessline 345
    [Credential-related source omitted]
  • Access to secretsline 345
    [Credential-related source omitted]
  • Network accessline 346
    https://api.github.com/repos/$OWNER/$REPO/actions/secrets \
  • Network accessline 370
    # Create a release curl -s -X POST \
  • Network accessline 371
    [Credential-related source omitted]
  • Access to secretsline 371
    [Credential-related source omitted]
  • Network accessline 372
    [Credential-related source omitted]
  • Access to secretsline 372
    [Credential-related source omitted]
  • Network accessline 373
    https://api.github.com/repos/$OWNER/$REPO/releases \
  • Network accessline 383
    # List releases curl -s \
  • Dangerous shell operationline 384
    [Credential-related source omitted]
  • Network accessline 384
    [Credential-related source omitted]
  • Access to secretsline 384
    [Credential-related source omitted]
  • Network accessline 385
    [Credential-related source omitted]
  • Access to secretsline 385
    [Credential-related source omitted]
  • Network accessline 386
    https://api.github.com/repos/$OWNER/$REPO/releases \
  • Network accessline 393
    # Upload a release asset (binary file) RELEASE_ID=<id_from_create_response> curl -s -X POST \
  • Network accessline 394
    [Credential-related source omitted]
  • Network accessline 395
    [Credential-related source omitted]
  • Access to secretsline 395
    [Credential-related source omitted]
  • Network accessline 396
    [Credential-related source omitted]
  • Access to secretsline 396
    [Credential-related source omitted]
  • Network accessline 397
    -H "Content-Type: application/octet-stream" \   "https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \   --data-binary @./dist/binary-amd64
  • Network accessline 398
    "https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \
  • Network accessline 420
    # List workflows curl -s \
  • Dangerous shell operationline 421
    [Credential-related source omitted]
  • Network accessline 421
    [Credential-related source omitted]
  • Access to secretsline 421
    [Credential-related source omitted]
  • Network accessline 422
    [Credential-related source omitted]
  • Access to secretsline 422
    [Credential-related source omitted]
  • Network accessline 423
    https://api.github.com/repos/$OWNER/$REPO/actions/workflows \
  • Network accessline 429
    # List recent runs curl -s \
  • Dangerous shell operationline 430
    [Credential-related source omitted]
  • Network accessline 430
    [Credential-related source omitted]
  • Access to secretsline 430
    [Credential-related source omitted]
  • Network accessline 431
    [Credential-related source omitted]
  • Access to secretsline 431
    [Credential-related source omitted]
  • Network accessline 432
    "https://api.github.com/repos/$OWNER/$REPO/actions/runs?per_page=10" \
  • Network accessline 438
    # Download failed run logs RUN_ID=<run_id> curl -s -L \
  • Network accessline 439
    [Credential-related source omitted]
  • Network accessline 440
    [Credential-related source omitted]
  • Access to secretsline 440
    [Credential-related source omitted]
  • Network accessline 441
    [Credential-related source omitted]
  • Access to secretsline 441
    [Credential-related source omitted]
  • Network accessline 442
    https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \
  • Network accessline 446
    # Re-run a failed workflow curl -s -X POST \
  • Network accessline 447
    [Credential-related source omitted]
  • Access to secretsline 447
    [Credential-related source omitted]
  • Access to secretsline 448
    [Credential-related source omitted]
  • Network accessline 449
    https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun
  • Network accessline 451
    # Re-run only failed jobs curl -s -X POST \
  • Network accessline 452
    [Credential-related source omitted]
  • Access to secretsline 452
    [Credential-related source omitted]
  • Access to secretsline 453
    [Credential-related source omitted]
  • Network accessline 454
    https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun-failed-jobs
  • Network accessline 456
    # Trigger a workflow manually (workflow_dispatch) WORKFLOW_ID=<workflow_id_or_filename> curl -s -X POST \
  • Network accessline 457
    [Credential-related source omitted]
  • Network accessline 458
    [Credential-related source omitted]
  • Access to secretsline 458
    [Credential-related source omitted]
  • Network accessline 459
    [Credential-related source omitted]
  • Access to secretsline 459
    [Credential-related source omitted]
  • Network accessline 460
    https://api.github.com/repos/$OWNER/$REPO/actions/workflows/$WORKFLOW_ID/dispatches \
  • Network accessline 476
    # Create a gist curl -s -X POST \
  • Network accessline 477
    [Credential-related source omitted]
  • Access to secretsline 477
    [Credential-related source omitted]
  • Network accessline 478
    [Credential-related source omitted]
  • Access to secretsline 478
    [Credential-related source omitted]
  • Network accessline 479
    https://api.github.com/gists \
  • Network accessline 488
    # List your gists curl -s \
  • Dangerous shell operationline 489
    [Credential-related source omitted]
  • Network accessline 489
    [Credential-related source omitted]
  • Access to secretsline 489
    [Credential-related source omitted]
  • Network accessline 490
    [Credential-related source omitted]
  • Access to secretsline 490
    [Credential-related source omitted]
  • Network accessline 491
    https://api.github.com/gists \
  • Network accessline 501
    | Action | gh | git + curl |
  • Network accessline 502
    |--------|-----|-----------| | Clone | `gh repo clone o/r` | `git clone https://github.com/o/r.git` | | Create repo | `gh repo create name --public` | `curl POST /user/repos` |
  • Network accessline 503
    | Clone | `gh repo clone o/r` | `git clone https://github.com/o/r.git` | | Create repo | `gh repo create name --public` | `curl POST /user/repos` | | Fork | `gh repo fork o/r --clone` | `curl POST /repos/o/r/forks` + `git clone` |
  • Network accessline 504
    | Create repo | `gh repo create name --public` | `curl POST /user/repos` |
  • Network accessline 505
    | Fork | `gh repo fork o/r --clone` | `curl POST /repos/o/r/forks` + `git clone` |
  • Network accessline 506
    | Repo info | `gh repo view o/r` | `curl GET /repos/o/r` |
  • Network accessline 507
    | Edit settings | `gh repo edit --...` | `curl PATCH /repos/o/r` |
  • Network accessline 508
    | Create release | `gh release create v1.0` | `curl POST /repos/o/r/releases` |
  • Network accessline 509
    | List workflows | `gh workflow list` | `curl GET /repos/o/r/actions/workflows` |
  • Network accessline 510
    | Rerun CI | `gh run rerun ID` | `curl POST /repos/o/r/actions/runs/ID/rerun` |
  • Network accessline 511
    | Set secret | `gh secret set KEY` | `curl PUT /repos/o/r/actions/secrets/KEY` (+ encryption) |

skills/github/github-repo-management/references/github-api-cheatsheet.md

  • Network accessline 3
    Base URL: `https://api.github.com`
  • Access to secretsline 5
    [Credential-related source omitted]
  • Access to secretsline 7
    [Credential-related source omitted]
  • Network accessline 92
    | Get release | GET | `/repos/{owner}/{repo}/releases/{id}` | | Delete release | DELETE | `/repos/{owner}/{repo}/releases/{id}` | | Upload asset | POST | `https://uploads.github.com/repos/{owner}/{repo}/releases/{id}/assets?name={filename}`
  • Network accessline 94
    | Upload asset | POST | `https://uploads.github.com/repos/{owner}/{repo}/releases/{id}/assets?name={filename}` |
  • Network accessline 132
    [Credential-related source omitted]
  • Network accessline 133
    [Credential-related source omitted]
  • Access to secretsline 133
    [Credential-related source omitted]
  • Network accessline 135
    ## Common curl Patterns
  • Network accessline 138
    [Credential-related source omitted]
  • Network accessline 139
    [Credential-related source omitted]
  • Access to secretsline 139
    [Credential-related source omitted]
  • Network accessline 140
    https://api.github.com/repos/$GH_OWNER/$GH_REPO
  • Network accessline 142
    # POST with JSON body curl -s -X POST \
  • Network accessline 143
    [Credential-related source omitted]
  • Access to secretsline 143
    [Credential-related source omitted]
  • Network accessline 144
    [Credential-related source omitted]
  • Access to secretsline 144
    [Credential-related source omitted]
  • Network accessline 145
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues \
  • Network accessline 148
    # PATCH (update) curl -s -X PATCH \
  • Network accessline 149
    [Credential-related source omitted]
  • Access to secretsline 149
    [Credential-related source omitted]
  • Network accessline 150
    [Credential-related source omitted]
  • Access to secretsline 150
    [Credential-related source omitted]
  • Network accessline 151
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues/42 \
  • Network accessline 154
    # DELETE curl -s -X DELETE \
  • Network accessline 155
    [Credential-related source omitted]
  • Access to secretsline 155
    [Credential-related source omitted]
  • Access to secretsline 156
    [Credential-related source omitted]
  • Network accessline 157
    https://api.github.com/repos/$GH_OWNER/$GH_REPO/issues/42/labels/bug
  • Network accessline 159
    # Parse JSON response with python3 curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])"
  • Dangerous shell operationline 160
    curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])"
  • Network accessline 160
    curl -s ... | python3 -c "import sys,json; data=json.load(sys.stdin); print(data['field'])"

skills/leisure/find-nearby/SKILL.md

  • Network accessline 62
    5. **For directions**, use the `directions_url` from results, or construct: `https://www.google.com/maps/dir/?api=1&origin=<LAT>,<LON>&destination=<LAT>,<LON>`

skills/leisure/find-nearby/scripts/find_nearby.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 27
    OVERPASS_URLS = [     "https://overpass-api.de/api/interpreter",
  • Network accessline 28
    "https://overpass-api.de/api/interpreter",
  • Network accessline 29
    "https://overpass.kumi.systems/api/interpreter",
  • Network accessline 30
    ] NOMINATIM_URL = "https://nominatim.openstreetmap.org/search" USER_AGENT = "GaussAgent/1.0 (find-nearby skill)"
  • Network accessline 31
    NOMINATIM_URL = "https://nominatim.openstreetmap.org/search"
  • Network accessline 36
    def _http_get(url: str) -> Any:     req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT})
  • Network accessline 37
    req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT})
  • Network accessline 38
    with urllib.request.urlopen(req, timeout=TIMEOUT) as r:
  • Network accessline 42
    def _http_post(url: str, data: str) -> Any:     req = urllib.request.Request(
  • Network accessline 43
    req = urllib.request.Request(
  • Network accessline 44
    url, data=data.encode(), headers={"User-Agent": USER_AGENT}     )     with urllib.request.urlopen(req, timeout=TIMEOUT) as r:
  • Network accessline 45
    )     with urllib.request.urlopen(req, timeout=TIMEOUT) as r:         return json.loads(r.read())
  • Network accessline 46
    with urllib.request.urlopen(req, timeout=TIMEOUT) as r:
  • Network accessline 110
    "lat": plat,             "lon": plon,             "maps_url": f"https://www.google.com/maps/search/?api=1&query={plat},{plon}",
  • Network accessline 111
    "lon": plon,             "maps_url": f"https://www.google.com/maps/search/?api=1&query={plat},{plon}",             "directions_url": f"https://www.google.com/maps/dir/?api=1&origin={lat},{lon}&destination={plat},{plon}",
  • Network accessline 112
    "maps_url": f"https://www.google.com/maps/search/?api=1&query={plat},{plon}",
  • Network accessline 113
    "directions_url": f"https://www.google.com/maps/dir/?api=1&origin={lat},{lon}&destination={plat},{plon}",

skills/mcp/mcporter/SKILL.md

  • Network accessline 8
    gauss:     tags: [MCP, Tools, API, Integrations, Interop]     homepage: https://mcporter.dev
  • Network accessline 9
    tags: [MCP, Tools, API, Integrations, Interop]     homepage: https://mcporter.dev prerequisites:
  • Network accessline 10
    homepage: https://mcporter.dev
  • Network accessline 17
    Use `mcporter` to discover, call, and manage [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers and tools directly from the terminal.
  • Network accessline 45
    mcporter auto-discovers servers configured by other MCP clients (Claude Desktop, Cursor, etc.) on the machine. To find new servers to use, browse registries like [mcpfinder.dev](https://mcpfinder.dev) or [mcp.so](https://mcp.so), then conne
  • Network accessline 48
    # Connect to any MCP server by URL (no config needed) mcporter list --http-url https://some-mcp-server.com --name my_server
  • Network accessline 49
    mcporter list --http-url https://some-mcp-server.com --name my_server
  • Network accessline 64
    # Ad-hoc HTTP server (no config needed) mcporter call https://api.example.com/mcp.fetch url=https://example.com
  • Network accessline 65
    mcporter call https://api.example.com/mcp.fetch url=https://example.com
  • Network accessline 67
    # Ad-hoc stdio server mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com
  • Network accessline 68
    mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com

skills/mcp/native-mcp/SKILL.md

  • Access to secretsline 73
    [Credential-related source omitted]
  • Network accessline 81
    mcp_servers:   server_name:     url: "https://my-server.example.com/mcp"   # (required) server URL
  • Network accessline 82
    server_name:     url: "https://my-server.example.com/mcp"   # (required) server URL     headers:                                     # (optional) HTTP headers
  • Network accessline 83
    url: "https://my-server.example.com/mcp"   # (required) server URL
  • Network accessline 165
    mcp_servers:   remote_api:     url: "https://mcp.example.com/mcp"
  • Network accessline 166
    remote_api:     url: "https://mcp.example.com/mcp"     headers:
  • Network accessline 167
    url: "https://mcp.example.com/mcp"
  • Access to secretsline 192
    [Credential-related source omitted]
  • Access to secretsline 202
    [Credential-related source omitted]
  • Access to secretsline 278
    [Credential-related source omitted]
  • Network accessline 287
    mcp_servers:   company_api:     url: "https://mcp.mycompany.com/v1/mcp"
  • Network accessline 288
    company_api:     url: "https://mcp.mycompany.com/v1/mcp"     headers:
  • Network accessline 289
    url: "https://mcp.mycompany.com/v1/mcp"
  • Access to secretsline 313
    [Credential-related source omitted]
  • Network accessline 315
    company_api:     url: "https://mcp.internal.company.com/mcp"
  • Network accessline 316
    url: "https://mcp.internal.company.com/mcp"

skills/media/gif-search/SKILL.md

  • Network accessline 1
    --- name: gif-search description: Search and download GIFs from Tenor using curl. No dependencies beyond curl and jq. Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat.
  • Network accessline 2
    name: gif-search description: Search and download GIFs from Tenor using curl. No dependencies beyond curl and jq. Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat. version: 1.1.0
  • Network accessline 3
    description: Search and download GIFs from Tenor using curl. No dependencies beyond curl and jq. Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat.
  • Access to secretsline 8
    [Credential-related source omitted]
  • Access to secretsline 24
    [Credential-related source omitted]
  • Network accessline 27
    Get a free API key at https://developers.google.com/tenor/guides/quickstart — the Google Cloud Console Tenor API key is free and has generous rate limits.
  • Access to secretsline 32
    [Credential-related source omitted]
  • Network accessline 37
    # Search and get GIF URLs curl -s "https://tenor.googleapis.com/v2/search?q=thumbs+up&limit=5&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.gif.url'
  • Network accessline 38
    [Credential-related source omitted]
  • Access to secretsline 38
    [Credential-related source omitted]
  • Network accessline 40
    # Get smaller/preview versions curl -s "https://tenor.googleapis.com/v2/search?q=nice+work&limit=3&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.tinygif.url'
  • Network accessline 41
    [Credential-related source omitted]
  • Access to secretsline 41
    [Credential-related source omitted]
  • Network accessline 47
    # Search and download the top result URL=$(curl -s "https://tenor.googleapis.com/v2/search?q=celebration&limit=1&key=${TENOR_API_KEY}" | jq -r '.results[0].media_formats.gif.url')
  • Network accessline 48
    [Credential-related source omitted]
  • Access to secretsline 48
    [Credential-related source omitted]
  • Network accessline 49
    curl -sL "$URL" -o celebration.gif
  • Network accessline 55
    [Credential-related source omitted]
  • Access to secretsline 55
    [Credential-related source omitted]
  • Access to secretsline 64
    [Credential-related source omitted]

skills/media/heartmula/SKILL.md

  • Network accessline 36
    cd ~/  # or desired directory git clone https://github.com/HeartMuLa/heartlib.git
  • Network accessline 37
    git clone https://github.com/HeartMuLa/heartlib.git
  • Network accessline 98
    - `torchtune` may report version `0.4.0+cpu` — this is just package metadata, it still uses CUDA via PyTorch - To verify GPU is being used, look for "CUDA memory" lines in the output (e.g. "CUDA memory before unloading: 6.20 GB") - **No GPU
  • Network accessline 100
    - **No GPU?** You can run on CPU with `--mula_device cpu --codec_device cpu`, but expect generation to be **extremely slow** (potentially 30-60+ minutes for a single song vs ~4 minutes on GPU). CPU mode also requires significant RAM (~12GB+
  • Network accessline 166
    ## Links - Repo: https://github.com/HeartMuLa/heartlib
  • Network accessline 167
    - Repo: https://github.com/HeartMuLa/heartlib
  • Network accessline 168
    - Models: https://huggingface.co/HeartMuLa
  • Network accessline 169
    - Paper: https://arxiv.org/abs/2601.10547

skills/media/songsee/SKILL.md

  • Network accessline 8
    gauss:     tags: [Audio, Visualization, Spectrogram, Music, Analysis]     homepage: https://github.com/steipete/songsee
  • Network accessline 9
    tags: [Audio, Visualization, Spectrogram, Music, Analysis]     homepage: https://github.com/steipete/songsee prerequisites:
  • Network accessline 10
    homepage: https://github.com/steipete/songsee
  • Network accessline 21
    Requires [Go](https://go.dev/doc/install):

skills/media/youtube-content/SKILL.md

  • Network accessline 21
    # JSON output with metadata python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
  • Network accessline 22
    python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
  • Network accessline 24
    # With timestamps python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --timestamps
  • Network accessline 25
    python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --timestamps
  • Network accessline 27
    # Plain text output (good for piping into further processing) python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --text-only
  • Network accessline 28
    python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --text-only
  • Network accessline 30
    # Specific language with fallback python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --language tr,en
  • Network accessline 31
    python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --language tr,en
  • Network accessline 33
    # Timestamped plain text python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --text-only --timestamps
  • Network accessline 34
    python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" --text-only --timestamps
  • Network accessline 43
    - `https://www.youtube.com/watch?v=VIDEO_ID`
  • Network accessline 44
    - `https://youtu.be/VIDEO_ID`
  • Network accessline 45
    - `https://youtube.com/shorts/VIDEO_ID`
  • Network accessline 46
    - `https://youtube.com/embed/VIDEO_ID`
  • Network accessline 47
    - `https://youtube.com/live/VIDEO_ID`

skills/media/youtube-content/scripts/fetch_transcript.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/mlops/cloud/lambda-labs/SKILL.md

  • Network accessline 46
    1. Create account at https://lambda.ai
  • Network accessline 53
    1. Go to https://cloud.lambda.ai/instances
  • Access to secretsline 67
    [Credential-related source omitted]
  • Network accessline 144
    # Configure with API key configuration = lambda_cloud_client.Configuration(     host="https://cloud.lambdalabs.com/api/v1",
  • Network accessline 145
    [Credential-related source omitted]
  • Network accessline 146
    host="https://cloud.lambdalabs.com/api/v1",
  • Access to secretsline 147
    [Credential-related source omitted]
  • Network accessline 224
    [Credential-related source omitted]
  • Access to secretsline 224
    [Credential-related source omitted]
  • Network accessline 225
    https://cloud.lambdalabs.com/api/v1/instance-types | jq
  • Network accessline 231
    [Credential-related source omitted]
  • Access to secretsline 231
    [Credential-related source omitted]
  • Network accessline 232
    -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \
  • Network accessline 244
    [Credential-related source omitted]
  • Access to secretsline 244
    [Credential-related source omitted]
  • Network accessline 245
    -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/terminate \
  • Access to secretsline 298
    [Credential-related source omitted]
  • Access to secretsline 308
    [Credential-related source omitted]
  • Network accessline 345
    # From local machine with tunnel ssh -L 8888:localhost:8888 ubuntu@<IP> # Open http://localhost:8888
  • Network accessline 347
    # Open http://localhost:8888
  • Network accessline 358
    # Clone repo git clone https://github.com/user/project
  • Network accessline 359
    git clone https://github.com/user/project
  • Network accessline 544
    - **Documentation**: https://docs.lambda.ai
  • Network accessline 545
    - **Console**: https://cloud.lambda.ai
  • Network accessline 546
    - **Pricing**: https://lambda.ai/instances
  • Network accessline 547
    - **Support**: https://support.lambdalabs.com
  • Network accessline 548
    - **Blog**: https://lambda.ai/blog

skills/mlops/cloud/lambda-labs/references/advanced-usage.md

  • Access to secretsline 16
    [Credential-related source omitted]
  • Access to secretsline 17
    [Credential-related source omitted]
  • Access to secretsline 18
    [Credential-related source omitted]
  • Network accessline 145
    [Credential-related source omitted]
  • Access to secretsline 145
    [Credential-related source omitted]
  • Network accessline 146
    [Credential-related source omitted]
  • Network accessline 147
    host="https://cloud.lambdalabs.com/api/v1",
  • Access to secretsline 148
    [Credential-related source omitted]
  • Access to secretsline 208
    [Credential-related source omitted]
  • Access to secretsline 253
    [Credential-related source omitted]
  • Network accessline 398
    # Install miniconda (if not present) wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
  • Network accessline 399
    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
  • Network accessline 465
    # Access at http://localhost:6006
  • Access to secretsline 474
    [Credential-related source omitted]
  • Access to secretsline 552
    [Credential-related source omitted]
  • Access to secretsline 554
    [Credential-related source omitted]
  • Access to secretsline 575
    [Credential-related source omitted]
  • Access to secretsline 579
    [Credential-related source omitted]
  • Access to secretsline 582
    [Credential-related source omitted]
  • Access to secretsline 603
    [Credential-related source omitted]
  • Access to secretsline 604
    [Credential-related source omitted]
  • Access to secretsline 606
    [Credential-related source omitted]
  • Access to secretsline 607
    [Credential-related source omitted]
  • Access to secretsline 610
    [Credential-related source omitted]

skills/mlops/cloud/lambda-labs/references/troubleshooting.md

  • Network accessline 11
    [Credential-related source omitted]
  • Network accessline 12
    [Credential-related source omitted]
  • Access to secretsline 12
    [Credential-related source omitted]
  • Network accessline 13
    https://cloud.lambdalabs.com/api/v1/instance-types | jq '.data | to_entries[] | select(.value.regions_with_capacity_available | length > 0) | .key'
  • Access to secretsline 47
    [Credential-related source omitted]
  • Network accessline 49
    [Credential-related source omitted]
  • Network accessline 50
    [Credential-related source omitted]
  • Access to secretsline 50
    [Credential-related source omitted]
  • Network accessline 51
    https://cloud.lambdalabs.com/api/v1/instance-types
  • Network accessline 81
    [Credential-related source omitted]
  • Network accessline 82
    [Credential-related source omitted]
  • Access to secretsline 82
    [Credential-related source omitted]
  • Network accessline 83
    https://cloud.lambdalabs.com/api/v1/instances | jq '.data[].ip'
  • Access to secretsline 93
    [Credential-related source omitted]
  • Access to secretsline 96
    [Credential-related source omitted]
  • Access to secretsline 97
    [Credential-related source omitted]
  • Access to secretsline 103
    [Credential-related source omitted]
  • Dangerous shell operationline 145
    sudo reboot
  • Network accessline 147
    # Reinstall NVIDIA drivers (if needed) wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Dangerous shell operationline 148
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Network accessline 148
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Dangerous shell operationline 149
    sudo reboot
  • Network accessline 194
    # Lambda Stack should have compatible versions # If mismatch, reinstall Lambda Stack wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Network accessline 195
    # If mismatch, reinstall Lambda Stack wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh - sudo reboot
  • Dangerous shell operationline 196
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Network accessline 196
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • Dangerous shell operationline 197
    sudo reboot
  • Network accessline 199
    # Or install specific PyTorch version pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html
  • Network accessline 200
    pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html
  • Access to secretsline 215
    [Credential-related source omitted]
  • Dangerous shell operationline 289
    find /lambda/nfs/storage/checkpoints -mtime +7 -delete
  • Network accessline 306
    # Or use SSH tunneling (recommended) ssh -L 6006:localhost:6006 ubuntu@<IP> # Access at http://localhost:6006
  • Network accessline 308
    # Access at http://localhost:6006
  • Network accessline 366
    # For CUDA packages, match CUDA version pip install torch --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 367
    pip install torch --index-url https://download.pytorch.org/whl/cu121
  • Dangerous shell operationline 380
    sudo apt install python3.11 python3.11-venv python3.11-dev
  • Network accessline 478
    [Credential-related source omitted]
  • Network accessline 479
    [Credential-related source omitted]
  • Access to secretsline 479
    [Credential-related source omitted]
  • Network accessline 480
    https://cloud.lambdalabs.com/api/v1/instances | jq '.data[].id'
  • Network accessline 517
    1. **Documentation**: https://docs.lambda.ai
  • Network accessline 518
    2. **Support**: https://support.lambdalabs.com

skills/mlops/cloud/modal/SKILL.md

  • Access to secretsline 241
    [Credential-related source omitted]
  • Network accessline 341
    - **Documentation**: https://modal.com/docs
  • Network accessline 342
    - **Examples**: https://github.com/modal-labs/modal-examples
  • Network accessline 343
    - **Pricing**: https://modal.com/pricing
  • Network accessline 344
    - **Discord**: https://discord.gg/modal

skills/mlops/cloud/modal/references/advanced-usage.md

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    image = modal.Image.debian_slim().pip_install(     "git+https://github.com/huggingface/transformers.git@main"
  • Network accessline 88
    "git+https://github.com/huggingface/transformers.git@main"
  • Network accessline 148
    @app.function(allow_concurrent_inputs=10) async def fetch_data(url: str):     async with aiohttp.ClientSession() as session:
  • Network accessline 149
    async def fetch_data(url: str):     async with aiohttp.ClientSession() as session:         return await session.get(url)
  • Network accessline 150
    async with aiohttp.ClientSession() as session:
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    [Credential-related source omitted]
  • Network accessline 499
    # Deployed endpoints accessible via HTTPS curl -X POST https://your-workspace--my-app-predict.modal.run \
  • Network accessline 500
    curl -X POST https://your-workspace--my-app-predict.modal.run \

skills/mlops/cloud/modal/references/troubleshooting.md

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    [Credential-related source omitted]
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    ).pip_install(     "torch",     index_url="https://download.pytorch.org/whl/cu121"  # Match CUDA
  • Network accessline 112
    "torch",     index_url="https://download.pytorch.org/whl/cu121"  # Match CUDA )
  • Network accessline 113
    index_url="https://download.pytorch.org/whl/cu121"  # Match CUDA
  • Access to secretsline 371
    [Credential-related source omitted]
  • Network accessline 482
    1. **Documentation**: https://modal.com/docs
  • Network accessline 483
    2. **Examples**: https://github.com/modal-labs/modal-examples
  • Network accessline 484
    3. **Discord**: https://discord.gg/modal
  • Network accessline 485
    4. **Status**: https://status.modal.com

skills/mlops/evaluation/huggingface-tokenizers/SKILL.md

  • Network accessline 502
    Browse all: https://huggingface.co/models?library=tokenizers
  • Network accessline 513
    - **Docs**: https://huggingface.co/docs/tokenizers
  • Network accessline 514
    - **GitHub**: https://github.com/huggingface/tokenizers ⭐ 9,000+
  • Network accessline 515
    - **Version**: 0.20.0+ - **Course**: https://huggingface.co/learn/nlp-course/chapter6/1 - **Paper**: BPE (Sennrich et al., 2016), WordPiece (Schuster & Nakajima, 2012)
  • Network accessline 516
    - **Course**: https://huggingface.co/learn/nlp-course/chapter6/1

skills/mlops/evaluation/lm-evaluation-harness/SKILL.md

  • Dangerous shell operationline 187
    Avoid for frequent eval (too slow):
  • Network accessline 487
    - GitHub: https://github.com/EleutherAI/lm-evaluation-harness
  • Network accessline 488
    - Docs: https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs
  • Network accessline 490
    - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard (uses this harness)

skills/mlops/evaluation/lm-evaluation-harness/references/api-evaluation.md

  • Access to secretsline 36
    [Credential-related source omitted]
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    lm_eval --model openai-chat-completions    --model_args      model=gpt-4-turbo,     base_url=https://api.openai.com/v1,
  • Network accessline 80
    base_url=https://api.openai.com/v1,\
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    [Credential-related source omitted]
  • Network accessline 150
    lm_eval --model anthropic-chat    --model_args      model=claude-3-5-sonnet-20241022,     base_url=https://api.anthropic.com,
  • Network accessline 153
    base_url=https://api.anthropic.com,\
  • Network accessline 196
    lm_eval --model local-completions    --model_args      model=meta-llama/Llama-2-7b-hf,     base_url=http://localhost:8000/v1,
  • Network accessline 199
    base_url=http://localhost:8000/v1,\
  • Network accessline 216
    lm_eval --model local-completions    --model_args      model=meta-llama/Llama-2-7b-hf,     base_url=http://localhost:8080/v1
  • Network accessline 219
    base_url=http://localhost:8080/v1 \
  • Network accessline 233
    lm_eval --model local-completions    --model_args      model=llama2:7b,     base_url=http://localhost:11434/v1
  • Network accessline 236
    base_url=http://localhost:11434/v1 \
  • Network accessline 249
    lm_eval --model local-completions    --model_args      model=llama2,     base_url=http://localhost:8080/v1
  • Network accessline 252
    base_url=http://localhost:8080/v1 \
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    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Access to secretsline 277
    [Credential-related source omitted]
  • Network accessline 300
    model = MyCustomAPI(     base_url="https://api.example.com/v1",
  • Network accessline 301
    base_url="https://api.example.com/v1",
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    [Credential-related source omitted]
  • Access to secretsline 417
    [Credential-related source omitted]
  • Access to secretsline 418
    [Credential-related source omitted]
  • Network accessline 441
    curl http://localhost:8000/v1/models
  • Network accessline 459
    lm_eval --model local-completions    --model_args      base_url=http://api.example.com/v1,
  • Network accessline 461
    base_url=http://api.example.com/v1,\
  • Network accessline 468
    lm_eval --model local-completions    --model_args      base_url=https://localhost:8000/v1,
  • Network accessline 470
    base_url=https://localhost:8000/v1,\
  • Network accessline 486
    - OpenAI API: https://platform.openai.com/docs/api-reference
  • Network accessline 487
    - Anthropic API: https://docs.anthropic.com/claude/reference

skills/mlops/evaluation/lm-evaluation-harness/references/distributed-eval.md

  • Network accessline 516
    - HuggingFace Accelerate: https://huggingface.co/docs/accelerate/
  • Network accessline 517
    - vLLM docs: https://docs.vllm.ai/
  • Network accessline 518
    - NeMo docs: https://docs.nvidia.com/nemo-framework/

skills/mlops/evaluation/nemo-curator/SKILL.md

  • Network accessline 380
    - **GitHub**: https://github.com/NVIDIA/NeMo-Curator ⭐ 500+
  • Network accessline 381
    - **Docs**: https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/

skills/mlops/evaluation/saelens/SKILL.md

  • Network accessline 18
    **GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars)
  • Network accessline 330
    Browse pre-trained SAE features at [neuronpedia.org](https://neuronpedia.org):
  • Network accessline 360
    ### Tutorials - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb)
  • Network accessline 361
    - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb)
  • Network accessline 362
    - [Training a Sparse Autoencoder](https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb)
  • Network accessline 363
    - [ARENA SAE Curriculum](https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab)
  • Network accessline 365
    ### Papers - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023)
  • Network accessline 366
    - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023)
  • Network accessline 367
    - [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) - Anthropic (2024)
  • Network accessline 368
    - [Sparse Autoencoders Find Highly Interpretable Features](https://arxiv.org/abs/2309.08600) - Cunningham et al. (ICLR 2024)
  • Network accessline 370
    ### Official Documentation - [SAELens Docs](https://jbloomaus.github.io/SAELens/)
  • Network accessline 371
    - [SAELens Docs](https://jbloomaus.github.io/SAELens/)
  • Network accessline 372
    - [Neuronpedia](https://neuronpedia.org) - Feature browser

skills/mlops/evaluation/saelens/references/README.md

  • Network accessline 13
    - **GitHub Repository**: https://github.com/jbloomAus/SAELens
  • Network accessline 14
    - **Neuronpedia**: https://neuronpedia.org (browse pre-trained SAE features)

skills/mlops/evaluation/saelens/references/tutorials.md

  • Network accessline 307
    ### Official Tutorials - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb)
  • Network accessline 308
    - [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb)
  • Network accessline 309
    - [Training SAEs](https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb)
  • Network accessline 310
    - [Logits Lens with Features](https://github.com/jbloomAus/SAELens/blob/main/tutorials/logits_lens_with_features.ipynb)
  • Network accessline 312
    ### ARENA Curriculum Comprehensive SAE course: https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab
  • Network accessline 313
    Comprehensive SAE course: https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab
  • Network accessline 315
    ### Key Papers - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023)
  • Network accessline 316
    - [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023)
  • Network accessline 317
    - [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) - Anthropic (2024)
  • Network accessline 318
    - [Sparse Autoencoders Find Interpretable Features](https://arxiv.org/abs/2309.08600) - ICLR 2024

skills/mlops/evaluation/weights-and-biases/SKILL.md

  • Access to secretsline 39
    [Credential-related source omitted]
  • Access to secretsline 546
    [Credential-related source omitted]
  • Network accessline 581
    - **Documentation**: https://docs.wandb.ai
  • Network accessline 582
    - **GitHub**: https://github.com/wandb/wandb (10.5k+ stars)
  • Network accessline 583
    - **Examples**: https://github.com/wandb/examples
  • Network accessline 584
    - **Community**: https://wandb.ai/community
  • Network accessline 585
    - **Discord**: https://wandb.me/discord

skills/mlops/evaluation/weights-and-biases/references/artifacts.md

  • Network accessline 433
    # HTTP reference dataset.add_reference('https://example.com/data.zip')
  • Network accessline 434
    dataset.add_reference('https://example.com/data.zip')
  • Network accessline 582
    - **Artifacts Documentation**: https://docs.wandb.ai/guides/artifacts
  • Network accessline 583
    - **Model Registry**: https://docs.wandb.ai/guides/model-registry
  • Network accessline 584
    - **Best Practices**: https://wandb.ai/site/articles/versioning-data-and-models-in-ml

skills/mlops/evaluation/weights-and-biases/references/integrations.md

  • Network accessline 696
    - **Integrations Guide**: https://docs.wandb.ai/guides/integrations
  • Network accessline 697
    - **HuggingFace**: https://docs.wandb.ai/guides/integrations/huggingface
  • Network accessline 698
    - **PyTorch Lightning**: https://docs.wandb.ai/guides/integrations/lightning
  • Network accessline 699
    - **Keras**: https://docs.wandb.ai/guides/integrations/keras
  • Network accessline 700
    - **Examples**: https://github.com/wandb/examples

skills/mlops/evaluation/weights-and-biases/references/sweeps.md

  • Access to secretsline 538
    [Credential-related source omitted]
  • Network accessline 845
    - **Sweeps Documentation**: https://docs.wandb.ai/guides/sweeps
  • Network accessline 846
    - **Configuration Reference**: https://docs.wandb.ai/guides/sweeps/configuration
  • Network accessline 847
    - **Examples**: https://github.com/wandb/examples/tree/master/examples/wandb-sweeps

skills/mlops/inference/gguf/SKILL.md

  • Network accessline 45
    # Clone llama.cpp git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 46
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 285
    client = OpenAI(     base_url="http://localhost:8080/v1",
  • Network accessline 286
    base_url="http://localhost:8080/v1",
  • Access to secretsline 287
    [Credential-related source omitted]
  • Network accessline 426
    - **Repository**: https://github.com/ggml-org/llama.cpp
  • Network accessline 427
    - **Python Bindings**: https://github.com/abetlen/llama-cpp-python
  • Network accessline 428
    - **Pre-quantized Models**: https://huggingface.co/TheBloke
  • Network accessline 429
    - **GGUF Converter**: https://huggingface.co/spaces/ggml-org/gguf-my-repo

skills/mlops/inference/gguf/references/advanced-usage.md

  • Access to secretsline 470
    [Credential-related source omitted]

skills/mlops/inference/gguf/references/troubleshooting.md

  • Dangerous shell operationline 22
    sudo apt install nvidia-cuda-toolkit
  • Network accessline 335
    # Use correct base URL format client = OpenAI(     base_url="http://localhost:8080/v1",  # Include /v1
  • Network accessline 336
    [Credential-related source omitted]
  • Network accessline 337
    base_url="http://localhost:8080/v1",  # Include /v1
  • Access to secretsline 338
    [Credential-related source omitted]
  • Network accessline 429
    1. **GitHub Issues**: https://github.com/ggml-org/llama.cpp/issues
  • Network accessline 430
    2. **Discussions**: https://github.com/ggml-org/llama.cpp/discussions

skills/mlops/inference/guidance/SKILL.md

  • Access to secretsline 275
    [Credential-related source omitted]
  • Access to secretsline 284
    [Credential-related source omitted]
  • Dangerous shell operationline 387
    "calculator": lambda expr: eval(expr),
  • Network accessline 564
    - **Documentation**: https://guidance.readthedocs.io
  • Network accessline 565
    - **GitHub**: https://github.com/guidance-ai/guidance (18k+ stars)
  • Network accessline 566
    - **Notebooks**: https://github.com/guidance-ai/guidance/tree/main/notebooks

skills/mlops/inference/guidance/references/backends.md

  • Access to secretsline 23
    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Access to secretsline 53
    [Credential-related source omitted]
  • Access to secretsline 90
    [Credential-related source omitted]
  • Access to secretsline 95
    [Credential-related source omitted]
  • Access to secretsline 120
    [Credential-related source omitted]
  • Network accessline 152
    lm = models.AzureOpenAI(     model="gpt-4o",     azure_endpoint="https://your-resource.openai.azure.com/",
  • Network accessline 153
    [Credential-related source omitted]
  • Network accessline 154
    azure_endpoint="https://your-resource.openai.azure.com/",
  • Access to secretsline 155
    [Credential-related source omitted]
  • Access to secretsline 519
    [Credential-related source omitted]
  • Access to secretsline 520
    [Credential-related source omitted]
  • Network accessline 550
    - **Anthropic Docs**: https://docs.anthropic.com
  • Network accessline 551
    - **OpenAI Docs**: https://platform.openai.com/docs
  • Network accessline 552
    - **Hugging Face Models**: https://huggingface.co/models
  • Network accessline 553
    - **llama.cpp**: https://github.com/ggerganov/llama.cpp
  • Network accessline 554
    - **GGUF Models**: https://huggingface.co/models?library=gguf

skills/mlops/inference/guidance/references/constraints.md

  • Network accessline 672
    - **Token Healing Paper**: https://arxiv.org/abs/2306.17648
  • Network accessline 673
    - **Guidance Docs**: https://guidance.readthedocs.io
  • Network accessline 674
    - **GitHub**: https://github.com/guidance-ai/guidance

skills/mlops/inference/guidance/references/examples.md

  • Dangerous shell operationline 414
    "calculator": lambda expr: eval(expr),
  • Network accessline 765
    - **Guidance Notebooks**: https://github.com/guidance-ai/guidance/tree/main/notebooks
  • Network accessline 766
    - **Guidance Docs**: https://guidance.readthedocs.io
  • Network accessline 767
    - **Community Examples**: https://github.com/guidance-ai/guidance/discussions

skills/mlops/inference/instructor/SKILL.md

  • Access to secretsline 344
    [Credential-related source omitted]
  • Access to secretsline 362
    [Credential-related source omitted]
  • Network accessline 378
    client = instructor.from_openai(     OpenAI(         base_url="http://localhost:11434/v1",
  • Network accessline 379
    [Credential-related source omitted]
  • Network accessline 380
    base_url="http://localhost:11434/v1",
  • Access to secretsline 381
    [Credential-related source omitted]
  • Network accessline 732
    - **Documentation**: https://python.useinstructor.com
  • Network accessline 733
    - **GitHub**: https://github.com/jxnl/instructor (15k+ stars)
  • Network accessline 734
    - **Cookbook**: https://python.useinstructor.com/examples

skills/mlops/inference/instructor/references/providers.md

  • Access to secretsline 16
    [Credential-related source omitted]
  • Network accessline 51
    client = instructor.from_openai(     OpenAI(         base_url="http://localhost:11434/v1",
  • Network accessline 52
    [Credential-related source omitted]
  • Network accessline 53
    base_url="http://localhost:11434/v1",
  • Access to secretsline 54
    [Credential-related source omitted]

skills/mlops/inference/instructor/references/validation.md

  • Network accessline 64
    messages=[{         "role": "user",         "content": "Extract: john@example.com, https://example.com"
  • Network accessline 65
    "role": "user",         "content": "Extract: john@example.com, https://example.com"     }],
  • Network accessline 66
    "content": "Extract: john@example.com, https://example.com"
  • Network accessline 176
    """Add https:// if missing."""         if not v.startswith(('http://', 'https://')):             v = f'https://{v}'
  • Network accessline 177
    if not v.startswith(('http://', 'https://')):             v = f'https://{v}'         return v
  • Network accessline 178
    v = f'https://{v}'
  • Network accessline 605
    - **Pydantic Docs**: https://docs.pydantic.dev/latest/concepts/validators/
  • Network accessline 606
    - **Instructor Examples**: https://python.useinstructor.com/examples

skills/mlops/inference/llama-cpp/SKILL.md

  • Network accessline 45
    # Or build from source git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 46
    git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 98
    # Client request curl http://localhost:8080/v1/chat/completions \
  • Network accessline 99
    curl http://localhost:8080/v1/chat/completions \
  • Network accessline 247
    **Find models**: https://huggingface.co/models?library=gguf
  • Network accessline 257
    - **GitHub**: https://github.com/ggerganov/llama.cpp
  • Network accessline 258
    - **Models**: https://huggingface.co/models?library=gguf
  • Network accessline 259
    - **Discord**: https://discord.gg/llama-cpp

skills/mlops/inference/llama-cpp/references/quantization.md

  • Network accessline 155
    **TheBloke** on HuggingFace: - https://huggingface.co/TheBloke
  • Network accessline 156
    - https://huggingface.co/TheBloke

skills/mlops/inference/llama-cpp/references/server.md

  • Network accessline 27
    curl http://localhost:8080/v1/chat/completions \
  • Network accessline 42
    curl http://localhost:8080/v1/chat/completions \
  • Network accessline 55
    FROM ubuntu:22.04 RUN apt-get update && apt-get install -y git build-essential RUN git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 56
    RUN apt-get update && apt-get install -y git build-essential RUN git clone https://github.com/ggerganov/llama.cpp WORKDIR /llama.cpp
  • Network accessline 57
    RUN git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 73
    # Server metrics endpoint curl http://localhost:8080/metrics
  • Network accessline 74
    curl http://localhost:8080/metrics
  • Network accessline 76
    # Health check curl http://localhost:8080/health
  • Network accessline 77
    curl http://localhost:8080/health
  • Network accessline 96
    server {     location / {         proxy_pass http://llama_cpp;
  • Network accessline 97
    location / {         proxy_pass http://llama_cpp;         proxy_read_timeout 300s;
  • Network accessline 98
    proxy_pass http://llama_cpp;

skills/mlops/inference/obliteratus/SKILL.md

  • Network accessline 39
    git clone https://github.com/elder-plinius/OBLITERATUS.git

skills/mlops/inference/outlines/SKILL.md

  • Access to secretsline 227
    [Credential-related source omitted]
  • Network accessline 644
    - **Documentation**: https://outlines-dev.github.io/outlines
  • Network accessline 645
    - **GitHub**: https://github.com/outlines-dev/outlines (8k+ stars)
  • Network accessline 646
    - **Discord**: https://discord.gg/R9DSu34mGd
  • Network accessline 647
    - **Blog**: https://blog.dottxt.co

skills/mlops/inference/outlines/references/backends.md

  • Access to secretsline 381
    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Access to secretsline 578
    [Credential-related source omitted]
  • Network accessline 612
    - **Transformers**: https://huggingface.co/docs/transformers
  • Network accessline 613
    - **llama.cpp**: https://github.com/ggerganov/llama.cpp
  • Network accessline 614
    - **vLLM**: https://docs.vllm.ai
  • Network accessline 615
    - **Outlines**: https://github.com/outlines-dev/outlines

skills/mlops/inference/outlines/references/examples.md

  • Network accessline 752
    self.requests.append(now)
  • Network accessline 771
    - **Outlines Documentation**: https://outlines-dev.github.io/outlines
  • Network accessline 772
    - **Pydantic Documentation**: https://docs.pydantic.dev
  • Network accessline 773
    - **GitHub Examples**: https://github.com/outlines-dev/outlines/tree/main/examples

skills/mlops/inference/outlines/references/json_generation.md

  • Network accessline 650
    - **Pydantic Docs**: https://docs.pydantic.dev
  • Network accessline 651
    - **JSON Schema**: https://json-schema.org
  • Network accessline 652
    - **Outlines GitHub**: https://github.com/outlines-dev/outlines

skills/mlops/inference/tensorrt-llm/SKILL.md

  • Network accessline 83
    # Client request curl -X POST http://localhost:8000/v1/chat/completions \
  • Network accessline 84
    curl -X POST http://localhost:8000/v1/chat/completions \
  • Network accessline 186
    - **Docs**: https://nvidia.github.io/TensorRT-LLM/
  • Network accessline 187
    - **GitHub**: https://github.com/NVIDIA/TensorRT-LLM
  • Network accessline 188
    - **Models**: https://huggingface.co/models?library=tensorrt_llm

skills/mlops/inference/tensorrt-llm/references/multi-gpu.md

  • Network accessline 142
    # Listens on http://localhost:8000
  • Network accessline 231
    # TensorRT-LLM built-in metrics curl http://localhost:8000/metrics
  • Network accessline 232
    curl http://localhost:8000/metrics

skills/mlops/inference/tensorrt-llm/references/serving.md

  • Access to secretsline 26
    [Credential-related source omitted]
  • Access to secretsline 34
    [Credential-related source omitted]
  • Network accessline 74
    curl -X POST http://localhost:8000/v1/chat/completions \
  • Network accessline 114
    curl -X POST http://localhost:8000/v1/chat/completions \
  • Network accessline 137
    curl -X POST http://localhost:8000/v1/completions \
  • Network accessline 160
    # Scrape metrics curl http://localhost:9090/metrics
  • Network accessline 161
    curl http://localhost:9090/metrics
  • Network accessline 176
    # Readiness probe curl http://localhost:8000/health/ready
  • Network accessline 177
    curl http://localhost:8000/health/ready
  • Network accessline 179
    # Liveness probe curl http://localhost:8000/health/live
  • Network accessline 180
    curl http://localhost:8000/health/live
  • Network accessline 182
    # Model info curl http://localhost:8000/v1/models
  • Network accessline 183
    curl http://localhost:8000/v1/models
  • Network accessline 306
    listen 80;     location / {         proxy_pass http://tensorrt_llm;
  • Network accessline 307
    location / {         proxy_pass http://tensorrt_llm;         proxy_read_timeout 300s;  # Long timeout for slow generations
  • Network accessline 308
    proxy_pass http://tensorrt_llm;
  • Access to secretsline 388
    [Credential-related source omitted]
  • Access to secretsline 391
    [Credential-related source omitted]
  • Access to secretsline 392
    [Credential-related source omitted]
  • Network accessline 394
    # Client request curl -X POST http://localhost:8000/v1/chat/completions \
  • Network accessline 395
    [Credential-related source omitted]
  • Access to secretsline 395
    [Credential-related source omitted]
  • Access to secretsline 396
    [Credential-related source omitted]
  • Network accessline 430
    # Check queue size curl http://localhost:9090/metrics | grep queue_size
  • Network accessline 431
    curl http://localhost:9090/metrics | grep queue_size
  • Network accessline 433
    # Check active requests curl http://localhost:9090/metrics | grep active_requests
  • Network accessline 434
    curl http://localhost:9090/metrics | grep active_requests

skills/mlops/inference/vllm/SKILL.md

  • Network accessline 41
    [Credential-related source omitted]
  • Network accessline 42
    [Credential-related source omitted]
  • Network accessline 43
    [Credential-related source omitted]
  • Access to secretsline 43
    [Credential-related source omitted]
  • Network accessline 102
    # Create test_load.py with sample requests # Run: locust -f test_load.py --host http://localhost:8000
  • Network accessline 103
    # Run: locust -f test_load.py --host http://localhost:8000
  • Network accessline 113
    curl http://localhost:9090/metrics | grep vllm
  • Network accessline 361
    - Official docs: https://docs.vllm.ai
  • Network accessline 362
    - GitHub: https://github.com/vllm-project/vllm
  • Network accessline 364
    - Community: https://discuss.vllm.ai

skills/mlops/inference/vllm/references/optimization.md

  • Network accessline 97
    curl http://localhost:9090/metrics | grep cache_hit

skills/mlops/inference/vllm/references/quantization.md

  • Network accessline 60
    [Credential-related source omitted]
  • Access to secretsline 60
    [Credential-related source omitted]

skills/mlops/inference/vllm/references/server-deployment.md

  • Network accessline 133
    location / {         proxy_pass http://vllm_backend;
  • Network accessline 134
    proxy_pass http://vllm_backend;
  • Network accessline 143
    # Metrics endpoint     location /metrics {         proxy_pass http://localhost:9090/metrics;
  • Network accessline 144
    location /metrics {         proxy_pass http://localhost:9090/metrics;     }
  • Network accessline 145
    proxy_pass http://localhost:9090/metrics;
  • Network accessline 225
    curl http://localhost:8000/health
  • Network accessline 231
    #!/bin/bash until curl -f http://localhost:8000/health; do
  • Network accessline 232
    until curl -f http://localhost:8000/health; do

skills/mlops/inference/vllm/references/troubleshooting.md

  • Network accessline 207
    curl http://localhost:8000/health
  • Dangerous shell operationline 222
    sudo ufw allow 8000
  • Network accessline 233
    client = OpenAI(     base_url="http://localhost:8000/v1",
  • Network accessline 234
    base_url="http://localhost:8000/v1",
  • Access to secretsline 235
    [Credential-related source omitted]
  • Network accessline 251
    session = requests.Session()
  • Network accessline 310
    # From node 1 to node 2 ping NODE2_IP nc -zv NODE2_IP 29500  # Check port accessibility
  • Network accessline 312
    nc -zv NODE2_IP 29500  # Check port accessibility
  • Network accessline 376
    # Prometheus metrics curl http://localhost:9090/metrics
  • Network accessline 377
    curl http://localhost:9090/metrics
  • Network accessline 379
    # Filter for specific metrics curl http://localhost:9090/metrics | grep vllm_time_to_first_token
  • Network accessline 380
    curl http://localhost:9090/metrics | grep vllm_time_to_first_token
  • Network accessline 393
    # Health check curl http://localhost:8000/health
  • Network accessline 394
    curl http://localhost:8000/health
  • Network accessline 396
    # Model info curl http://localhost:8000/v1/models
  • Network accessline 397
    curl http://localhost:8000/v1/models
  • Network accessline 399
    # Test completion curl http://localhost:8000/v1/completions \
  • Network accessline 400
    curl http://localhost:8000/v1/completions \

skills/mlops/models/audiocraft/SKILL.md

  • Network accessline 50
    # From GitHub (latest) pip install git+https://github.com/facebookresearch/audiocraft.git
  • Network accessline 51
    pip install git+https://github.com/facebookresearch/audiocraft.git
  • Network accessline 563
    - **GitHub**: https://github.com/facebookresearch/audiocraft
  • Network accessline 564
    - **Paper (MusicGen)**: https://arxiv.org/abs/2306.05284
  • Network accessline 565
    - **Paper (AudioGen)**: https://arxiv.org/abs/2209.15352
  • Network accessline 566
    - **HuggingFace**: https://huggingface.co/facebook/musicgen-small
  • Network accessline 567
    - **Demo**: https://huggingface.co/spaces/facebook/MusicGen

skills/mlops/models/audiocraft/references/advanced-usage.md

  • Network accessline 73
    # Clone AudioCraft git clone https://github.com/facebookresearch/audiocraft.git
  • Network accessline 74
    git clone https://github.com/facebookresearch/audiocraft.git

skills/mlops/models/audiocraft/references/troubleshooting.md

  • Network accessline 14
    # Or from GitHub pip install git+https://github.com/facebookresearch/audiocraft.git
  • Network accessline 15
    pip install git+https://github.com/facebookresearch/audiocraft.git
  • Dangerous shell operationline 28
    sudo apt-get install ffmpeg
  • Network accessline 50
    # Install matching PyTorch pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 51
    pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 53
    # For CUDA 11.8 pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
  • Network accessline 54
    pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
  • Access to secretsline 71
    [Credential-related source omitted]
  • Access to secretsline 108
    [Credential-related source omitted]
  • Access to secretsline 111
    [Credential-related source omitted]
  • Network accessline 491
    1. **GitHub Issues**: https://github.com/facebookresearch/audiocraft/issues
  • Network accessline 492
    2. **HuggingFace Forums**: https://discuss.huggingface.co
  • Network accessline 493
    3. **Paper**: https://arxiv.org/abs/2306.05284

skills/mlops/models/clip/SKILL.md

  • Network accessline 44
    pip install git+https://github.com/openai/CLIP.git
  • Network accessline 251
    - **GitHub**: https://github.com/openai/CLIP ⭐ 25,300+
  • Network accessline 252
    - **Paper**: https://arxiv.org/abs/2103.00020
  • Network accessline 253
    - **Colab**: https://colab.research.google.com/github/openai/clip/

skills/mlops/models/clip/references/applications.md

  • Network accessline 205
    - **Paper**: https://arxiv.org/abs/2103.00020
  • Network accessline 206
    - **GitHub**: https://github.com/openai/CLIP
  • Network accessline 207
    - **Colab**: https://colab.research.google.com/github/openai/clip/

skills/mlops/models/llava/SKILL.md

  • Network accessline 45
    # Clone repository git clone https://github.com/haotian-liu/LLaVA
  • Network accessline 46
    git clone https://github.com/haotian-liu/LLaVA
  • Network accessline 140
    # Access at http://localhost:7860
  • Network accessline 301
    - **GitHub**: https://github.com/haotian-liu/LLaVA ⭐ 23,000+
  • Network accessline 302
    - **Paper**: https://arxiv.org/abs/2304.08485
  • Network accessline 303
    - **Demo**: https://llava.hliu.cc
  • Network accessline 304
    - **Models**: https://huggingface.co/liuhaotian

skills/mlops/models/llava/references/training.md

  • Network accessline 195
    - **Training script**: https://github.com/haotian-liu/LLaVA/tree/main/scripts
  • Network accessline 196
    - **Data format**: https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md
  • Network accessline 197
    - **Paper**: https://arxiv.org/abs/2304.08485

skills/mlops/models/segment-anything/SKILL.md

  • Network accessline 47
    # From GitHub pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 48
    pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 60
    # ViT-H (largest, most accurate) - 2.4GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
  • Network accessline 61
    wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
  • Network accessline 63
    # ViT-L (medium) - 1.2GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
  • Network accessline 64
    wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
  • Network accessline 66
    # ViT-B (smallest, fastest) - 375MB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
  • Network accessline 67
    wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
  • Network accessline 499
    - **GitHub**: https://github.com/facebookresearch/segment-anything
  • Network accessline 500
    - **Paper**: https://arxiv.org/abs/2304.02643
  • Network accessline 501
    - **Demo**: https://segment-anything.com
  • Network accessline 502
    - **SAM 2 (Video)**: https://github.com/facebookresearch/segment-anything-2
  • Network accessline 503
    - **HuggingFace**: https://huggingface.co/facebook/sam-vit-huge

skills/mlops/models/segment-anything/references/advanced-usage.md

  • Network accessline 10
    pip install git+https://github.com/facebookresearch/segment-anything-2.git
  • Network accessline 52
    pip install groundingdino-py pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 53
    pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 349
    # MedSAM is a fine-tuned SAM for medical images # https://github.com/bowang-lab/MedSAM
  • Network accessline 350
    # https://github.com/bowang-lab/MedSAM

skills/mlops/models/segment-anything/references/troubleshooting.md

  • Network accessline 16
    # Install PyTorch with CUDA pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 17
    pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 30
    # Install from GitHub pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 31
    pip install git+https://github.com/facebookresearch/segment-anything.git
  • Network accessline 33
    # Or clone and install git clone https://github.com/facebookresearch/segment-anything.git
  • Network accessline 34
    git clone https://github.com/facebookresearch/segment-anything.git
  • Network accessline 63
    # Download correct checkpoint wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
  • Network accessline 64
    wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
  • Network accessline 472
    1. **GitHub Issues**: https://github.com/facebookresearch/segment-anything/issues
  • Network accessline 473
    2. **HuggingFace Forums**: https://discuss.huggingface.co
  • Network accessline 474
    3. **Paper**: https://arxiv.org/abs/2304.02643

skills/mlops/models/stable-diffusion/SKILL.md

  • Network accessline 519
    - **Documentation**: https://huggingface.co/docs/diffusers
  • Network accessline 520
    - **Repository**: https://github.com/huggingface/diffusers
  • Network accessline 521
    - **Model Hub**: https://huggingface.co/models?library=diffusers
  • Network accessline 522
    - **Discord**: https://discord.gg/diffusers

skills/mlops/models/stable-diffusion/references/troubleshooting.md

  • Network accessline 27
    # Install matching xformers pip install xformers --index-url https://download.pytorch.org/whl/cu121  # For CUDA 12.1
  • Network accessline 28
    pip install xformers --index-url https://download.pytorch.org/whl/cu121  # For CUDA 12.1
  • Network accessline 30
    # Or build from source pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
  • Network accessline 31
    pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
  • Network accessline 43
    # Reinstall PyTorch with correct CUDA pip uninstall torch torchvision pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 45
    pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
  • Network accessline 383
    # Use mirror if available export HF_ENDPOINT=https://hf-mirror.com
  • Network accessline 384
    export HF_ENDPOINT=https://hf-mirror.com
  • Dangerous shell operationline 397
    rm -rf ~/.cache/huggingface/hub
  • Network accessline 541
    1. **Documentation**: https://huggingface.co/docs/diffusers
  • Network accessline 542
    2. **GitHub Issues**: https://github.com/huggingface/diffusers/issues
  • Network accessline 543
    3. **Discord**: https://discord.gg/diffusers
  • Network accessline 544
    4. **Forum**: https://discuss.huggingface.co

skills/mlops/models/whisper/SKILL.md

  • Dangerous shell operationline 49
    # Ubuntu: sudo apt install ffmpeg
  • Network accessline 314
    - **GitHub**: https://github.com/openai/whisper ⭐ 72,900+
  • Network accessline 315
    - **Paper**: https://arxiv.org/abs/2212.04356
  • Network accessline 316
    - **Model Card**: https://github.com/openai/whisper/blob/main/model-card.md

skills/mlops/models/whisper/references/languages.md

  • Network accessline 187
    - **Paper**: https://arxiv.org/abs/2212.04356
  • Network accessline 188
    - **GitHub**: https://github.com/openai/whisper
  • Network accessline 189
    - **Model Card**: https://github.com/openai/whisper/blob/main/model-card.md

skills/mlops/research/dspy/SKILL.md

  • Network accessline 34
    # Latest development version pip install git+https://github.com/stanfordnlp/dspy.git
  • Network accessline 35
    pip install git+https://github.com/stanfordnlp/dspy.git
  • Access to secretsline 295
    [Credential-related source omitted]
  • Access to secretsline 307
    [Credential-related source omitted]
  • Network accessline 316
    lm = dspy.OllamaLocal(     model="llama3.1",     base_url="http://localhost:11434"
  • Network accessline 317
    model="llama3.1",     base_url="http://localhost:11434" )
  • Network accessline 318
    base_url="http://localhost:11434"
  • Network accessline 581
    - **Documentation**: https://dspy.ai
  • Network accessline 582
    - **GitHub**: https://github.com/stanfordnlp/dspy (22k+ stars)
  • Network accessline 583
    - **Discord**: https://discord.gg/XCGy2WDCQB

skills/mlops/research/dspy/references/examples.md

  • Dangerous shell operationline 165
    result = eval(expression, {"__builtins__": {}}, {})
  • Network accessline 661
    - **Documentation**: https://dspy.ai
  • Network accessline 662
    - **Examples Repo**: https://github.com/stanfordnlp/dspy/tree/main/examples
  • Network accessline 663
    - **Discord**: https://discord.gg/XCGy2WDCQB

skills/mlops/research/dspy/references/modules.md

  • Dangerous shell operationline 135
    return eval(expression)

skills/mlops/research/dspy/references/optimizers.md

  • Network accessline 564
    - **Paper**: "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines" - **GitHub**: https://github.com/stanfordnlp/dspy
  • Network accessline 565
    - **GitHub**: https://github.com/stanfordnlp/dspy
  • Network accessline 566
    - **Discord**: https://discord.gg/XCGy2WDCQB

skills/mlops/training/accelerate/SKILL.md

  • Network accessline 327
    - Docs: https://huggingface.co/docs/accelerate
  • Network accessline 328
    - GitHub: https://github.com/huggingface/accelerate
  • Network accessline 329
    - Version: 1.11.0+ - Tutorial: "Accelerate your scripts" - Examples: https://github.com/huggingface/accelerate/tree/main/examples
  • Network accessline 330
    - Tutorial: "Accelerate your scripts" - Examples: https://github.com/huggingface/accelerate/tree/main/examples - Used by: HuggingFace Transformers, TRL, PEFT, all HF libraries
  • Network accessline 331
    - Examples: https://github.com/huggingface/accelerate/tree/main/examples

skills/mlops/training/accelerate/references/custom-plugins.md

  • Network accessline 450
    - Accelerate Plugins: https://huggingface.co/docs/accelerate/package_reference/kwargs
  • Network accessline 451
    - DeepSpeed Config: https://www.deepspeed.ai/docs/config-json/
  • Network accessline 452
    - FSDP Guide: https://pytorch.org/docs/stable/fsdp.html
  • Network accessline 453
    - Custom Training Loops: https://huggingface.co/docs/accelerate/usage_guides/training_tpu

skills/mlops/training/accelerate/references/megatron-integration.md

  • Network accessline 18
    # Clone Megatron-LM repository git clone https://github.com/NVIDIA/Megatron-LM.git
  • Network accessline 19
    git clone https://github.com/NVIDIA/Megatron-LM.git
  • Network accessline 23
    # Install Apex (NVIDIA optimizations) git clone https://github.com/NVIDIA/apex
  • Network accessline 24
    git clone https://github.com/NVIDIA/apex
  • Network accessline 486
    - Megatron-LM: https://github.com/NVIDIA/Megatron-LM
  • Network accessline 487
    - Accelerate Megatron docs: https://huggingface.co/docs/accelerate/usage_guides/megatron_lm
  • Network accessline 489
    - NVIDIA Apex: https://github.com/NVIDIA/apex

skills/mlops/training/accelerate/references/performance.md

  • Access to secretsline 303
    [Credential-related source omitted]
  • Network accessline 522
    - Accelerate Performance: https://huggingface.co/docs/accelerate/usage_guides/performance
  • Network accessline 523
    - PyTorch Profiler: https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html
  • Network accessline 524
    - NCCL Tuning: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
  • Network accessline 525
    - Flash Attention: https://github.com/Dao-AILab/flash-attention

skills/mlops/training/axolotl/SKILL.md

  • Network accessline 67
    **Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https://github.com/axolotl-ai-

skills/mlops/training/axolotl/references/api.md

  • Network accessline 9
    **URL:** https://docs.axolotl.ai/docs/api/cli.cloud.modal_.html
  • Network accessline 40
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.base.html
  • Network accessline 99
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.input_output.html
  • Network accessline 135
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.completion.html
  • Network accessline 171
    **URL:** https://docs.axolotl.ai/docs/api/utils.collators.core.html
  • Network accessline 182
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.data.batch_dataset_fetcher.html
  • Network accessline 230
    **URL:** https://docs.axolotl.ai/docs/api/core.datasets.chat.html
  • Network accessline 259
    **URL:** https://docs.axolotl.ai/docs/api/utils.freeze.html
  • Network accessline 307
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.unsloth_.html
  • Network accessline 318
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.datasets.html
  • Network accessline 380
    **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.llama3x.html
  • Network accessline 393
    **URL:** https://docs.axolotl.ai/docs/api/datasets.html
  • Network accessline 424
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.bradley_terry.llama3.html
  • Network accessline 435
    chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
  • Network accessline 448
    **URL:** https://docs.axolotl.ai/docs/api/common.datasets.html
  • Network accessline 502
    **URL:** https://docs.axolotl.ai/docs/api/cli.train.html
  • Network accessline 534
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.fetch.html
  • Network accessline 557
    **URL:** https://docs.axolotl.ai/docs/api/utils.tokenization.html
  • Network accessline 597
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.sampler.html
  • Network accessline 610
    Repeat random sampler (similar to the one implemented in https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds sequence parallelism functionality; i.e., duplicating data across ranks in the same sequence parall
  • Network accessline 665
    **URL:** https://docs.axolotl.ai/docs/api/evaluate.html
  • Network accessline 699
    **URL:** https://docs.axolotl.ai/docs/api/utils.optimizers.adopt.html
  • Network accessline 708
    Copied from https://github.com/iShohei220/adopt
  • Network accessline 747
    **URL:** https://docs.axolotl.ai/docs/api/prompt_tokenizers.html
  • Network accessline 834
    **URL:** https://docs.axolotl.ai/docs/api/cli.art.html
  • Network accessline 856
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.perplexity.html
  • Network accessline 869
    Calculate perplexity as defined in https://huggingface.co/docs/transformers/en/perplexity. This is a custom variant that doesn’t re-tokenize the input or re-load the model.
  • Network accessline 889
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.train.html
  • Network accessline 937
    **URL:** https://docs.axolotl.ai/docs/api/cli.vllm_serve.html
  • Network accessline 974
    **URL:** https://docs.axolotl.ai/docs/api/convert.html
  • Network accessline 1031
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.utils.html
  • Network accessline 1069
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.pygmalion.html
  • Network accessline 1106
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.mlflow_.html
  • Network accessline 1130
    **URL:** https://docs.axolotl.ai/docs/api/loaders.adapter.html
  • Network accessline 1160
    **URL:** https://docs.axolotl.ai/docs/api/cli.cloud.base.html
  • Network accessline 1182
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_flash.html
  • Network accessline 1195
    From: https://github.com/dvlab-research/LongLoRA/blob/main/llama_attn_replace.py
  • Network accessline 1223
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_patch_multipack.html
  • Network accessline 1236
    **URL:** https://docs.axolotl.ai/docs/api/cli.inference.html
  • Network accessline 1286
    **URL:** https://docs.axolotl.ai/docs/api/loaders.tokenizer.html
  • Network accessline 1304
    Ref: https://github.com/huggingface/transformers/issues/27974#issuecomment-1854188941
  • Network accessline 1326
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.sweeps.html
  • Network accessline 1353
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chatml.html
  • Network accessline 1369
    chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
  • Network accessline 1401
    **URL:** https://docs.axolotl.ai/docs/api/cli.quantize.html
  • Network accessline 1424
    **URL:** https://docs.axolotl.ai/docs/api/utils.dict.html
  • Network accessline 1437
    Remove null from a dictionary-like obj or list. These can appear due to Dataset loading causing schema merge. See https://github.com/axolotl-ai-cloud/axolotl/pull/2909
  • Network accessline 1455
    **URL:** https://docs.axolotl.ai/docs/api/
  • Network accessline 1501
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.lora_kernels.html
  • Network accessline 1565
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.stablelm_attn_hijack_flash.html
  • Network accessline 1597
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.rng_state_loader.html
  • Network accessline 1608
    See https://github.com/huggingface/transformers/pull/37162
  • Network accessline 1625
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.utils.html
  • Network accessline 1636
    **URL:** https://docs.axolotl.ai/docs/api/core.training_args.html
  • Network accessline 1690
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.btlm_attn_hijack_flash.html
  • Network accessline 1703
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.passthrough.html
  • Network accessline 1716
    **URL:** https://docs.axolotl.ai/docs/api/kernels.swiglu.html
  • Network accessline 1730
    See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202).
  • Network accessline 1732
    Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation.
  • Network accessline 1754
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.grpo.trainer.html
  • Network accessline 1808
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.user_defined.html
  • Network accessline 1854
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.training.html
  • Network accessline 1894
    **URL:** https://docs.axolotl.ai/docs/api/utils.quantization.html
  • Network accessline 1960
    **URL:** https://docs.axolotl.ai/docs/api/logging_config.html
  • Network accessline 2007
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.stepwise_supervised.html
  • Network accessline 2037
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.model.html
  • Network accessline 2075
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.enums.html
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    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.trl.html
  • Network accessline 2170
    **URL:** https://docs.axolotl.ai/docs/api/utils.schedulers.html
  • Network accessline 2196
    Implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf) Create a schedule with a learning rate that decreases following the values of the cosine function between
  • Network accessline 2251
    **URL:** https://docs.axolotl.ai/docs/api/cli.merge_lora.html
  • Network accessline 2284
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_w_system.html
  • Network accessline 2346
    **URL:** https://docs.axolotl.ai/docs/api/loaders.patch_manager.html
  • Network accessline 2398
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.peft.html
  • Network accessline 2444
    **URL:** https://docs.axolotl.ai/docs/api/common.const.html
  • Network accessline 2455
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.user_defined.html
  • Network accessline 2468
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.base.html
  • Network accessline 2481
    **URL:** https://docs.axolotl.ai/docs/api/cli.delinearize_llama4.html
  • Network accessline 2506
    **URL:** https://docs.axolotl.ai/docs/api/integrations.base.html
  • Network accessline 2662
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.chat_template.html
  • Network accessline 2760
    **URL:** https://docs.axolotl.ai/docs/api/kernels.quantize.html
  • Network accessline 2790
    **URL:** https://docs.axolotl.ai/docs/api/integrations.spectrum.args.html
  • Network accessline 2814
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_chat.html
  • Network accessline 2877
    **URL:** https://docs.axolotl.ai/docs/api/utils.collators.mamba.html
  • Network accessline 2899
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.messages.chat.html
  • Network accessline 2928
    **URL:** https://docs.axolotl.ai/docs/api/train.html
  • Network accessline 2998
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.load.html
  • Network accessline 3022
    **URL:** https://docs.axolotl.ai/docs/api/loaders.model.html
  • Network accessline 3065
    **URL:** https://docs.axolotl.ai/docs/api/utils.distributed.html
  • Network accessline 3143
    **URL:** https://docs.axolotl.ai/docs/api/cli.config.html
  • Network accessline 3193
    **URL:** https://docs.axolotl.ai/docs/api/cli.checks.html
  • Network accessline 3225
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.llama2_chat.html
  • Network accessline 3239
    Prompt Strategy for finetuning Llama2 chat models see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation.
  • Network accessline 3241
    This implementation is based on the Vicuna PR and the fastchat repo, see also: https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847
  • Network accessline 3253
    Tokenizing strategy for Llama2 prompts. adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
  • Network accessline 3255
    A class that manages prompt templates and keeps all conversation history. copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
  • Network accessline 3297
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.html
  • Network accessline 3308
    **URL:** https://docs.axolotl.ai/docs/api/cli.utils.args.html
  • Network accessline 3351
    **URL:** https://docs.axolotl.ai/docs/api/integrations.grokfast.optimizer.html
  • Network accessline 3362
    **URL:** https://docs.axolotl.ai/docs/api/core.builders.causal.html
  • Network accessline 3389
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.user_defined.html
  • Network accessline 3402
    **URL:** https://docs.axolotl.ai/docs/api/cli.evaluate.html
  • Network accessline 3434
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.utils.html
  • Network accessline 3463
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.alpaca_instruct.html
  • Network accessline 3476
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.lisa.html
  • Network accessline 3481
    Adapted from https://github.com/OptimalScale/LMFlow/pull/701 for HF transformers & Axolotl Arxiv: https://arxiv.org/abs/2403.17919 License: Apache 2.0
  • Network accessline 3487
    **URL:** https://docs.axolotl.ai/docs/api/models.mamba.modeling_mamba.html
  • Network accessline 3498
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.metharme.html
  • Network accessline 3535
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mamba.html
  • Network accessline 3563
    **URL:** https://docs.axolotl.ai/docs/api/utils.ctx_managers.sequence_parallel.html
  • Network accessline 3638
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.qat.html
  • Network accessline 3670
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.zephyr.html
  • Network accessline 3683
    **URL:** https://docs.axolotl.ai/docs/api/kernels.utils.html
  • Network accessline 3694
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.multipack.html
  • Network accessline 3707
    **URL:** https://docs.axolotl.ai/docs/api/cli.main.html
  • Network accessline 3753
    cli.main.fetch(directory, dest)
  • Network accessline 3765
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.optimizer.html
  • Network accessline 3797
    **URL:** https://docs.axolotl.ai/docs/api/integrations.kd.trainer.html
  • Network accessline 3835
    **URL:** https://docs.axolotl.ai/docs/api/integrations.lm_eval.args.html
  • Network accessline 3859
    **URL:** https://docs.axolotl.ai/docs/api/integrations.cut_cross_entropy.args.html
  • Network accessline 3883
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mistral_attn_hijack_flash.html
  • Network accessline 3896
    **URL:** https://docs.axolotl.ai/docs/api/loaders.constants.html
  • Network accessline 3907
    **URL:** https://docs.axolotl.ai/docs/api/utils.bench.html
  • Network accessline 3929
    **URL:** https://docs.axolotl.ai/docs/api/utils.trainer.html
  • Network accessline 3994
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.config.html
  • Network accessline 4024
    **URL:** https://docs.axolotl.ai/docs/api/cli.args.html
  • Network accessline 4095
    **URL:** https://docs.axolotl.ai/docs/api/common.architectures.html
  • Network accessline 4106
    **URL:** https://docs.axolotl.ai/docs/api/cli.merge_sharded_fsdp_weights.html
  • Network accessline 4157
    **URL:** https://docs.axolotl.ai/docs/api/utils.data.streaming.html
  • Network accessline 4168
    **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.chatml.html
  • Network accessline 4181
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.chatml.html
  • Network accessline 4221
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.trl.html
  • Network accessline 4241
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_attn_hijack_xformers.html
  • Network accessline 4248
    Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
  • Network accessline 4254
    **URL:** https://docs.axolotl.ai/docs/api/kernels.geglu.html
  • Network accessline 4269
    See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202).
  • Network accessline 4271
    Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation.
  • Network accessline 4293
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.profiler.html
  • Network accessline 4320
    **URL:** https://docs.axolotl.ai/docs/api/kernels.lora.html
  • Network accessline 4336
    See “LoRA: Low-Rank Adaptation of Large Language Models” (https://arxiv.org/abs/2106.09685).
  • Network accessline 4338
    Credit to unsloth (https://unsloth.ai/) for inspiration for this implementation.
  • Network accessline 4422
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.trainer_fsdp_optim.html
  • Network accessline 4446
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.multimodal.html
  • Network accessline 4481
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.llama3.html
  • Network accessline 4497
    chatml transforms for datasets with system, input, chosen, rejected ex. https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
  • Network accessline 4529
    **URL:** https://docs.axolotl.ai/docs/api/core.chat.format.shared.html
  • Network accessline 4542
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.llama_expand_mask.html
  • Network accessline 4549
    expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
  • Network accessline 4555
    **URL:** https://docs.axolotl.ai/docs/api/core.chat.messages.html
  • Network accessline 4621
    **URL:** https://docs.axolotl.ai/docs/api/core.datasets.transforms.chat_builder.html
  • Network accessline 4653
    **URL:** https://docs.axolotl.ai/docs/api/utils.chat_templates.html
  • Network accessline 4664
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.dpo.trainer.html
  • Network accessline 4697
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_disk.html
  • Network accessline 4778
    **URL:** https://docs.axolotl.ai/docs/api/utils.samplers.multipack.html
  • Network accessline 4865
    **URL:** https://docs.axolotl.ai/docs/api/core.trainers.mixins.scheduler.html
  • Network accessline 4902
    **URL:** https://docs.axolotl.ai/docs/api/utils.collators.batching.html
  • Network accessline 4983
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orcamini.html
  • Network accessline 4992
    Prompt Strategy for finetuning Orca Mini (v2) models see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information
  • Network accessline 5015
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.dpo.chat_template.html
  • Network accessline 5046
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.relora.html
  • Network accessline 5053
    Implements the ReLoRA training procedure from https://arxiv.org/abs/2307.05695, minus the initial full fine-tune.
  • Network accessline 5068
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.transformers_fa_utils.html
  • Network accessline 5078
    see https://github.com/huggingface/transformers/pull/35834
  • Network accessline 5099
    **URL:** https://docs.axolotl.ai/docs/api/utils.collators.mm_chat.html
  • Network accessline 5130
    **URL:** https://docs.axolotl.ai/docs/api/utils.lora.html
  • Network accessline 5154
    **URL:** https://docs.axolotl.ai/docs/api/utils.model_shard_quant.html
  • Network accessline 5191
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.gradient_checkpointing.offload_cpu.html
  • Network accessline 5216
    **URL:** https://docs.axolotl.ai/docs/api/core.builders.base.html
  • Network accessline 5247
    **URL:** https://docs.axolotl.ai/docs/api/core.builders.rl.html
  • Network accessline 5269
    **URL:** https://docs.axolotl.ai/docs/api/utils.schemas.integrations.html
  • Network accessline 5326
    **URL:** https://docs.axolotl.ai/docs/api/utils.data.sft.html
  • Network accessline 5350
    **URL:** https://docs.axolotl.ai/docs/api/integrations.liger.args.html
  • Network accessline 5374
    **URL:** https://docs.axolotl.ai/docs/api/monkeypatch.mixtral.html
  • Network accessline 5385
    **URL:** https://docs.axolotl.ai/docs/api/cli.preprocess.html
  • Network accessline 5417
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.kto.llama3.html
  • Network accessline 5457
    **URL:** https://docs.axolotl.ai/docs/api/prompt_strategies.orpo.chat_template.html
  • Network accessline 5517
    **URL:** https://docs.axolotl.ai/docs/api/loaders.processor.html
  • Network accessline 5528
    **URL:** https://docs.axolotl.ai/docs/api/utils.callbacks.comet_.html

skills/mlops/training/axolotl/references/dataset-formats.md

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    **URL:** https://docs.axolotl.ai/docs/dataset-formats/tokenized.html
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    **URL:** https://docs.axolotl.ai/docs/dataset-formats/index.html
  • Network accessline 247
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/conversation.html
  • Network accessline 377
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/pretraining.html
  • Network accessline 411
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/template_free.html
  • Network accessline 502
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/
  • Network accessline 715
    **URL:** https://docs.axolotl.ai/docs/dataset-formats
  • Network accessline 928
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/inst_tune.html
  • Network accessline 1004
    **URL:** https://docs.axolotl.ai/docs/dataset-formats/stepwise_supervised.html

skills/mlops/training/axolotl/references/other.md

  • Network accessline 9
    **URL:** https://docs.axolotl.ai/docs/mixed_precision.html
  • Network accessline 95
    **URL:** https://docs.axolotl.ai/docs/faq.html
  • Network accessline 150
    A: There seems to be a wheel issue with FA2 2.8.0 on CUDA 12.4. Try CUDA 12.6 instead or downgrade to FA2 2.7.4. Please refer to the upstream issue: https://github.com/Dao-AILab/flash-attention/issues/1717.
  • Network accessline 158
    A: We use torch APIs to retrieve this information. You can see https://docs.pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management for more information.
  • Network accessline 246
    **URL:** https://docs.axolotl.ai/docs/installation.html
  • Network accessline 264
    Follow the instructions at: https://pytorch.org/get-started/locally/
  • Network accessline 294
    Install PyTorch: https://pytorch.org/get-started/locally/
  • Dangerous shell operationline 310
    curl -LsSf https://astral.sh/uv/install.sh | sh
  • Network accessline 310
    curl -LsSf https://astral.sh/uv/install.sh | sh
  • Network accessline 332
    **URL:** https://docs.axolotl.ai/docs/dataset_preprocessing.html
  • Network accessline 358
    **URL:** https://docs.axolotl.ai/docs/inference.html
  • Network accessline 421
    **URL:** https://docs.axolotl.ai/docs/multimodal.html
  • Network accessline 535
    **URL:** https://docs.axolotl.ai/docs/reward_modelling.html
  • Network accessline 604
    **URL:** https://docs.axolotl.ai/docs/rlhf.html
  • Network accessline 634
    Paper: https://arxiv.org/abs/2403.07691
  • Network accessline 724
    **URL:** https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 810
    **URL:** https://docs.axolotl.ai/docs/quantize.html
  • Network accessline 864
    **URL:** https://docs.axolotl.ai/docs/nccl.html
  • Network accessline 911
    **URL:** https://docs.axolotl.ai/docs/multi-node.html
  • Network accessline 1001
    **URL:** https://docs.axolotl.ai/docs/dataset_loading.html
  • Access to secretsline 1053
    [Credential-related source omitted]
  • Network accessline 1099
    The path should start with https://.
  • Network accessline 1143
    **URL:** https://docs.axolotl.ai/docs/multi-gpu.html
  • Network accessline 1238
    **URL:** https://docs.axolotl.ai/docs/ray-integration.html
  • Network accessline 1324
    **URL:** https://docs.axolotl.ai/docs/sequence_parallelism.html
  • Network accessline 1391
    **URL:** https://docs.axolotl.ai/docs/qat.html
  • Network accessline 1423
    **URL:** https://docs.axolotl.ai/docs/fsdp_qlora.html
  • Network accessline 1454
    **URL:** https://docs.axolotl.ai/docs/custom_integrations.html
  • Network accessline 1474
    See https://github.com/apple/ml-cross-entropy
  • Network accessline 1520
    See https://github.com/ironjr/grokfast
  • Network accessline 1546
    To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation: https://github.com/vllm-project/llm-compressor/blob/main/README.md
  • Network accessline 1560
    https://github.com/vllm-project/llm-compressor
  • Network accessline 1566
    See https://github.com/EleutherAI/lm-evaluation-harness
  • Network accessline 1572
    See https://github.com/linkedin/Liger-Kernel
  • Network accessline 1580
    See https://github.com/cognitivecomputations/spectrum
  • Network accessline 1598
    See this repo for an example: https://github.com/axolotl-ai-cloud/diff-transformer
  • Network accessline 1609
    pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@8a1a0ec"
  • Network accessline 1627
    journal      = {arXiv},   year         = {2024},   url          = {https://arxiv.org/abs/2411.09009},
  • Network accessline 1628
    year         = {2024},   url          = {https://arxiv.org/abs/2411.09009}, }
  • Network accessline 1629
    url          = {https://arxiv.org/abs/2411.09009},
  • Network accessline 1637
    **URL:** https://docs.axolotl.ai/docs/config-reference.html
  • Network accessline 1793
    process_reward_model: bool | None # Coefficient to incentivize the reward model to output mean-zero rewards (proposed by # https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.
  • Network accessline 1794
    # Coefficient to incentivize the reward model to output mean-zero rewards (proposed by # https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. center_rewards_coefficient: float | None
  • Network accessline 1795
    # https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.
  • Network accessline 1868
    field_messages: str | None   # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).
  • Network accessline 1869
    # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).   field_tools: str | None
  • Network accessline 1870
    # schema](https://json-schema.org/learn/getting-started-step-by-step).
  • Network accessline 2025
    field_messages: str | None   # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).
  • Network accessline 2026
    # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).   field_tools: str | None
  • Network accessline 2027
    # schema](https://json-schema.org/learn/getting-started-step-by-step).
  • Network accessline 2209
    field_messages: str | None   # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).
  • Network accessline 2210
    # Key containing the tools (default: "tools"). Must be a list[dict] and follow [JSON   # schema](https://json-schema.org/learn/getting-started-step-by-step).   field_tools: str | None
  • Network accessline 2211
    # schema](https://json-schema.org/learn/getting-started-step-by-step).
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    [Credential-related source omitted]
  • Access to secretsline 2291
    [Credential-related source omitted]
  • Network accessline 2358
    # Whether to use gradient checkpointing. Available options are: true, false, 'offload', # 'offload_disk'. # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
  • Network accessline 2359
    # 'offload_disk'. # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing gradient_checkpointing: Literal['offload', 'offload_disk'] | bool | None = False
  • Network accessline 2360
    # https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
  • Network accessline 2425
    # Whether to use xformers attention patch https://github.com/facebookresearch/xformers
  • Network accessline 2426
    xformers_attention: bool | None # Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/ # torch.nn.functional.scaled_dot_product_attention.html
  • Network accessline 2427
    # Whether to use scaled-dot-product attention https://pytorch.org/docs/stable/generated/
  • Network accessline 2428
    # torch.nn.functional.scaled_dot_product_attention.html sdp_attention: bool | None # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
  • Network accessline 2429
    sdp_attention: bool | None # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf s2_attention: bool | None
  • Network accessline 2430
    # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
  • Network accessline 2432
    flex_attention: bool | None flex_attn_compile_kwargs: dict[str, Any] | None # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention
  • Network accessline 2433
    flex_attn_compile_kwargs: dict[str, Any] | None # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention flash_attention: bool | None
  • Network accessline 2434
    # Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention
  • Network accessline 2457
    # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2458
    # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2459
    lora_mlp_kernel: bool | None # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2460
    # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html lora_qkv_kernel: bool | None
  • Network accessline 2461
    # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2462
    lora_qkv_kernel: bool | None # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2463
    # Apply custom LoRA autograd functions and activation function Triton kernels for speed # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html lora_o_kernel: bool | None
  • Network accessline 2464
    # and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html
  • Network accessline 2540
    # a single GPU's VRAM. E.g., if 4 GPUs are available, set this value to 2 to split each # sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized # subsequences. See https://docs.axolotl.ai/docs/sequence_paral
  • Network accessline 2541
    # sequence into two equal-sized subsequences, or set to 4 to split into four equal-sized # subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more # details.
  • Network accessline 2542
    # subsequences. See https://docs.axolotl.ai/docs/sequence_parallelism.html for more
  • Network accessline 2609
    # Logging frequency logging_steps: int | None # Stop training after this many evaluation losses have increased in a row. https://huggi
  • Network accessline 2610
    logging_steps: int | None # Stop training after this many evaluation losses have increased in a row. https://huggi # ngface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppin
  • Network accessline 2611
    # Stop training after this many evaluation losses have increased in a row. https://huggi
  • Network accessline 2620
    use_tensorboard: bool | None # Enable the pytorch profiler to capture the first N steps of training to the # output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more
  • Network accessline 2621
    # Enable the pytorch profiler to capture the first N steps of training to the # output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more # information. Snapshots can be visualized @ https://pytorch.org/memory_viz
  • Network accessline 2622
    # output_dir. see https://pytorch.org/blog/understanding-gpu-memory-1/ for more
  • Network accessline 2623
    # information. Snapshots can be visualized @ https://pytorch.org/memory_viz
  • Network accessline 2631
    # throughput of non-padding tokens. include_tkps: bool | None = True # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to
  • Network accessline 2632
    include_tkps: bool | None = True # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to # add noise to embeddings. Currently only supported on Llama and Mistral
  • Network accessline 2633
    # NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to
  • Network accessline 2717
    # Add plugins to extend the pipeline. See `src/axolotl/integrations` for the available # plugins or doc below for more details. # https://docs.axolotl.ai/docs/custom_integrations.html
  • Network accessline 2718
    # plugins or doc below for more details. # https://docs.axolotl.ai/docs/custom_integrations.html plugins: list[str] | None
  • Network accessline 2719
    # https://docs.axolotl.ai/docs/custom_integrations.html
  • Network accessline 2815
    # A list of token indices to fine-tune on the `embed_tokens` layer. Otherwise, a dict # mapping an embedding layer name to its trainable token indices. See # https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-trai
  • Network accessline 2816
    # mapping an embedding layer name to its trainable token indices. See # https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train- # tokens-alongside-lora
  • Network accessline 2817
    # https://huggingface.co/docs/peft/v0.17.0/en/developer_guides/lora#efficiently-train-
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    [Credential-related source omitted]
  • Network accessline 3036
    **URL:** https://docs.axolotl.ai
  • Network accessline 3100
    title = {Axolotl: Open Source LLM Post-Training},   author = {{Axolotl maintainers and contributors}},   url = {https://github.com/axolotl-ai-cloud/axolotl},
  • Network accessline 3101
    author = {{Axolotl maintainers and contributors}},   url = {https://github.com/axolotl-ai-cloud/axolotl},   license = {Apache-2.0},
  • Network accessline 3102
    url = {https://github.com/axolotl-ai-cloud/axolotl},
  • Network accessline 3112
    **URL:** https://docs.axolotl.ai/docs/getting-started.html
  • Network accessline 3219
    **URL:** https://docs.axolotl.ai/docs/multipack.html
  • Network accessline 3291
    **URL:** https://docs.axolotl.ai/docs/batch_vs_grad.html
  • Network accessline 3351
    **URL:** https://docs.axolotl.ai/docs/debugging.html
  • Network accessline 3493
    **URL:** https://docs.axolotl.ai/docs/docker.html

skills/mlops/training/flash-attention/SKILL.md

  • Network accessline 363
    - Paper: "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (NeurIPS 2022) - Paper: "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning" (ICLR 2024) - Blog: https://tridao.me/blog/202
  • Network accessline 364
    - Paper: "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning" (ICLR 2024) - Blog: https://tridao.me/blog/2024/flash3/ - GitHub: https://github.com/Dao-AILab/flash-attention
  • Network accessline 365
    - Blog: https://tridao.me/blog/2024/flash3/
  • Network accessline 366
    - GitHub: https://github.com/Dao-AILab/flash-attention
  • Network accessline 367
    - PyTorch docs: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html

skills/mlops/training/gauss-atropos-environments/SKILL.md

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    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Network accessline 47
    2. **Self-hosted VLLM endpoint** — Ask for their base URL (e.g., `http://localhost:8000/v1`) and model name. Set `--openai.server_type vllm`.
  • Network accessline 49
    4. **Local Atropos training server** — For `serve` mode with a live training loop. Default `http://localhost:8000/v1`.
  • Access to secretsline 61
    [Credential-related source omitted]
  • Access to secretsline 63
    [Credential-related source omitted]
  • Network accessline 221
    # SERVE — Full training loop (connects to Atropos API server) python environments/my_env.py serve --openai.base_url http://localhost:8000/v1
  • Network accessline 222
    python environments/my_env.py serve --openai.base_url http://localhost:8000/v1
  • Dangerous shell operationline 231
    # EVALUATE — Standalone eval (runs setup + evaluate only)

skills/mlops/training/gauss-atropos-environments/references/atropos-base-env.md

  • Dangerous shell operationline 65
    - Common reward patterns: LLM-judge (~40), regex-extract (~35), code-exec (~12)

skills/mlops/training/gauss-atropos-environments/references/usage-patterns.md

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    # Terminal 3: Environment python environments/your_env.py serve \   --openai.base_url http://localhost:8000/v1 \
  • Network accessline 169
    python environments/your_env.py serve    --openai.base_url http://localhost:8000/v1
  • Network accessline 170
    --openai.base_url http://localhost:8000/v1 \

skills/mlops/training/grpo-rl-training/README.md

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    - [TRL Documentation](https://huggingface.co/docs/trl)
  • Network accessline 81
    - [DeepSeek R1 Paper](https://arxiv.org/abs/2501.12948)
  • Network accessline 82
    - [Open R1 Implementation](https://github.com/huggingface/open-r1)
  • Network accessline 83
    - [Unsloth (2-3x faster)](https://docs.unsloth.ai/)
  • Network accessline 91
    Orchestra Research For questions or improvements, see https://orchestra.com
  • Network accessline 92
    For questions or improvements, see https://orchestra.com

skills/mlops/training/grpo-rl-training/SKILL.md

  • Network accessline 538
    **Official Documentation:** - TRL GRPO Trainer: https://huggingface.co/docs/trl/grpo_trainer
  • Network accessline 539
    - TRL GRPO Trainer: https://huggingface.co/docs/trl/grpo_trainer
  • Network accessline 540
    - DeepSeek R1 Paper: https://arxiv.org/abs/2501.12948
  • Network accessline 541
    - Unsloth Docs: https://docs.unsloth.ai/
  • Network accessline 543
    **Example Repositories:** - Open R1 Implementation: https://github.com/huggingface/open-r1
  • Network accessline 544
    - Open R1 Implementation: https://github.com/huggingface/open-r1
  • Network accessline 545
    - TRL Examples: https://github.com/huggingface/trl/tree/main/examples

skills/mlops/training/grpo-rl-training/templates/basic_grpo_training.py

  • Executable scriptline 1
    Executable file

skills/mlops/training/peft/SKILL.md

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    - **GitHub**: https://github.com/huggingface/peft
  • Network accessline 431
    - **Docs**: https://huggingface.co/docs/peft
  • Network accessline 432
    - **LoRA Paper**: arXiv:2106.09685 - **QLoRA Paper**: arXiv:2305.14314 - **Models**: https://huggingface.co/models?library=peft
  • Network accessline 434
    - **Models**: https://huggingface.co/models?library=peft

skills/mlops/training/peft/references/troubleshooting.md

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    # Or compile from source for specific CUDA git clone https://github.com/TimDettmers/bitsandbytes.git
  • Network accessline 19
    git clone https://github.com/TimDettmers/bitsandbytes.git
  • Network accessline 218
    **Error**: `ValueError: We couldn't connect to 'https://huggingface.co'`
  • Network accessline 457
    1. **Check PEFT GitHub Issues**: https://github.com/huggingface/peft/issues
  • Network accessline 458
    2. **HuggingFace Forums**: https://discuss.huggingface.co/
  • Network accessline 459
    3. **PEFT Documentation**: https://huggingface.co/docs/peft

skills/mlops/training/pytorch-fsdp/SKILL.md

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    **Pattern 5:** Groups# By default collectives operate on the default group (also called the world) and require all processes to enter the distributed function call. However, some workloads can benefit from more fine-grained communication. T
  • Network accessline 64
    **Pattern 6:** Warning Safe concurrent usage: When using multiple process groups with the NCCL backend, the user must ensure a globally consistent execution order of collectives across ranks. If multiple threads within a process issue colle

skills/mlops/training/pytorch-fsdp/references/other.md

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    **URL:** https://pytorch.org/docs/stable/notes/ddp.html
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    **URL:** https://pytorch.org/docs/stable/
  • Network accessline 92
    **URL:** https://pytorch.org/docs/stable/distributed.algorithms.join.html
  • Network accessline 159
    **URL:** https://pytorch.org/docs/stable/distributed._dist2.html
  • Network accessline 380
    **URL:** https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html
  • Network accessline 578
    **URL:** https://pytorch.org/docs/stable/distributed.html
  • Network accessline 692
    pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. As of now, the only options we support is ProcessGroupNCCL.Option
  • Network accessline 834
    See Using multiple NCCL communicators concurrently <https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#using-multiple-nccl-communicators-concurrently> for more details.
  • Network accessline 842
    pg_options (ProcessGroupOptions, optional) – process group options specifying what additional options need to be passed in during the construction of specific process groups. i.e. for the nccl backend, is_high_priority_stream can be specifi
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    [Credential-related source omitted]
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    **URL:** https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
  • Network accessline 1963
    **URL:** https://pytorch.org/docs/stable/ddp_comm_hooks.html
  • Network accessline 2167
    **URL:** https://pytorch.org/docs/stable/distributed.checkpoint.html
  • Network accessline 2899
    **URL:** https://pytorch.org/docs/stable/distributed.tensor.html
  • Network accessline 3289
    **URL:** https://pytorch.org/docs/stable/fsdp.html
  • Network accessline 3372
    device_mesh (Optional[DeviceMesh]) – DeviceMesh can be used as an alternative to process_group. When device_mesh is passed, FSDP will use the underlying process groups for all-gather and reduce-scatter collective communications. Therefore,
  • Network accessline 3728
    **URL:** https://pytorch.org/docs/stable/distributed.optim.html
  • Network accessline 3853
    **URL:** https://pytorch.org/docs/stable/distributed.elastic.html
  • Network accessline 3868
    **URL:** https://pytorch.org/docs/stable/distributed.pipelining.html
  • Network accessline 4074
    The Interleaved 1F1B schedule. See https://arxiv.org/pdf/2104.04473 for details. Will perform one forward and one backward on the microbatches in steady state and supports multiple stages per rank. When microbatches are ready for multiple l
  • Network accessline 4082
    Breadth-First Pipeline Parallelism. See https://arxiv.org/abs/2211.05953 for details. Similar to Interleaved 1F1B, Looped BFS supports multiple stages per rank. What is different is that when microbatches are ready for multiple local stages
  • Network accessline 4084
    The Interleaved Zero Bubble schedule. See https://arxiv.org/pdf/2401.10241 for details. Will perform one forward and one backward on inputs for the microbatches in steady state and supports multiple stages per rank. Uses the backward for we
  • Network accessline 4088
    The Zero Bubble schedule (ZBV variant). See https://arxiv.org/pdf/2401.10241 Section 6 for details.
  • Network accessline 4096
    The DualPipeV schedule. A more efficient schedule variant based on the DualPipe schedule introduced by DeepSeek in https://arxiv.org/pdf/2412.19437
  • Network accessline 4120
    **URL:** https://pytorch.org/docs/stable/distributed.tensor.parallel.html
  • Network accessline 4127
    Tensor Parallelism(TP) is built on top of the PyTorch DistributedTensor (DTensor)[https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/README.md] and provides different parallelism styles: Colwise, Rowwise, and Sequence Par

skills/mlops/training/pytorch-lightning/SKILL.md

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    - Docs: https://lightning.ai/docs/pytorch/stable/
  • Network accessline 343
    - GitHub: https://github.com/Lightning-AI/pytorch-lightning ⭐ 29,000+
  • Network accessline 344
    - Version: 2.5.5+ - Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples - Discord: https://discord.gg/lightning-ai
  • Network accessline 345
    - Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples
  • Network accessline 346
    - Discord: https://discord.gg/lightning-ai

skills/mlops/training/pytorch-lightning/references/callbacks.md

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    - Callback API: https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html
  • Network accessline 435
    - Built-in callbacks: https://lightning.ai/docs/pytorch/stable/api_references.html#callbacks
  • Network accessline 436
    - Examples: https://github.com/Lightning-AI/pytorch-lightning/tree/master/examples/callbacks

skills/mlops/training/pytorch-lightning/references/distributed.md

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    - Distributed strategies: https://lightning.ai/docs/pytorch/stable/accelerators/gpu_intermediate.html
  • Network accessline 488
    - FSDP guide: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/fsdp.html
  • Network accessline 489
    - DeepSpeed: https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/deepspeed.html
  • Network accessline 490
    - Multi-node: https://lightning.ai/docs/pytorch/stable/clouds/cluster.html

skills/mlops/training/pytorch-lightning/references/hyperparameter-tuning.md

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    - Ray Tune + Lightning: https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html
  • Network accessline 554
    - Optuna: https://optuna.readthedocs.io/
  • Network accessline 555
    - WandB Sweeps: https://docs.wandb.ai/guides/sweeps
  • Network accessline 556
    - Lightning Tuner: https://lightning.ai/docs/pytorch/stable/tuning.html

skills/mlops/training/simpo/SKILL.md

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    # Install PyTorch 2.2.2 # Visit: https://pytorch.org/get-started/locally/
  • Network accessline 26
    # Visit: https://pytorch.org/get-started/locally/
  • Network accessline 28
    # Install alignment-handbook git clone https://github.com/huggingface/alignment-handbook.git
  • Network accessline 29
    git clone https://github.com/huggingface/alignment-handbook.git
  • Network accessline 216
    - Paper: https://arxiv.org/abs/2405.14734 (NeurIPS 2024)
  • Network accessline 217
    - GitHub: https://github.com/princeton-nlp/SimPO
  • Network accessline 218
    - Models: https://huggingface.co/princeton-nlp
  • Network accessline 219
    - Alignment Handbook: https://github.com/huggingface/alignment-handbook

skills/mlops/training/simpo/references/datasets.md

  • Network accessline 476
    - HuggingFace Datasets: https://huggingface.co/datasets
  • Network accessline 477
    - Alignment Handbook: https://github.com/huggingface/alignment-handbook
  • Network accessline 478
    - UltraFeedback: https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized

skills/mlops/training/simpo/references/hyperparameters.md

  • Network accessline 451
    - SimPO paper: https://arxiv.org/abs/2405.14734
  • Network accessline 452
    - Alignment Handbook: https://github.com/huggingface/alignment-handbook

skills/mlops/training/simpo/references/loss-functions.md

  • Network accessline 348
    - SimPO paper: https://arxiv.org/abs/2405.14734
  • Network accessline 349
    - DPO paper: https://arxiv.org/abs/2305.18290
  • Network accessline 350
    - Implementation: https://github.com/princeton-nlp/SimPO

skills/mlops/training/slime/SKILL.md

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    git clone https://github.com/THUDM/slime.git
  • Network accessline 463
    - **Documentation**: https://thudm.github.io/slime/
  • Network accessline 464
    - **GitHub**: https://github.com/THUDM/slime
  • Network accessline 465
    - **Blog**: https://lmsys.org/blog/2025-07-09-slime/

skills/mlops/training/slime/references/api-reference.md

  • Network accessline 389
    - Documentation: https://thudm.github.io/slime/
  • Network accessline 390
    - GitHub: https://github.com/THUDM/slime
  • Network accessline 391
    - Blog: https://lmsys.org/blog/2025-07-09-slime/

skills/mlops/training/slime/references/troubleshooting.md

  • Network accessline 384
    - GitHub Issues: https://github.com/THUDM/slime/issues
  • Network accessline 385
    - Documentation: https://thudm.github.io/slime/

skills/mlops/training/torchtitan/SKILL.md

  • Network accessline 25
    # From source (latest features, requires PyTorch nightly) git clone https://github.com/pytorch/torchtitan
  • Network accessline 26
    git clone https://github.com/pytorch/torchtitan
  • Network accessline 33
    # Get HF token from https://huggingface.co/settings/tokens
  • Network accessline 189
    USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
  • Network accessline 357
    - GitHub: https://github.com/pytorch/torchtitan
  • Network accessline 358
    - Paper: https://arxiv.org/abs/2410.06511
  • Network accessline 359
    - ICLR 2025: https://iclr.cc/virtual/2025/poster/29620
  • Network accessline 360
    - PyTorch Forum: https://discuss.pytorch.org/c/distributed/torchtitan/44

skills/mlops/training/torchtitan/references/checkpoint.md

  • Network accessline 156
    Checkpoints saved with `last_save_model_only = true` can be loaded directly into [torchtune](https://github.com/pytorch/torchtune) for fine-tuning.

skills/mlops/training/torchtitan/references/float8.md

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    USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
  • Network accessline 127
    Check [torchao microbenchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#performance) for forward+backward pass speedups on "layer norm => linear => sigmoid" for different M,N,K sizes.
  • Network accessline 133
    For NVIDIA Blackwell GPUs, TorchTitan supports MXFP8 (Microscaling FP8) for both dense and MoE models. See [docs/mxfp8.md](https://github.com/pytorch/torchtitan/blob/main/docs/mxfp8.md) for details.

skills/mlops/training/trl-fine-tuning/SKILL.md

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    - Docs: https://huggingface.co/docs/trl/
  • Network accessline 450
    - GitHub: https://github.com/huggingface/trl
  • Network accessline 453
    - "Direct Preference Optimization: Your Language Model is Secretly a Reward Model" (DPO, 2023)   - "Group Relative Policy Optimization" (GRPO, 2024) - Examples: https://github.com/huggingface/trl/tree/main/examples/scripts
  • Network accessline 455
    - Examples: https://github.com/huggingface/trl/tree/main/examples/scripts

skills/mlops/training/trl-fine-tuning/references/dpo-variants.md

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    - DPO paper: https://arxiv.org/abs/2305.18290
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    - IPO paper: https://arxiv.org/abs/2310.12036
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    - TRL docs: https://huggingface.co/docs/trl/dpo_trainer

skills/mlops/training/trl-fine-tuning/references/online-rl.md

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    - PPO paper: https://arxiv.org/abs/1707.06347
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    - GRPO paper: https://arxiv.org/abs/2402.03300
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    - TRL docs: https://huggingface.co/docs/trl/

skills/mlops/training/trl-fine-tuning/references/reward-modeling.md

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    - InstructGPT paper: https://arxiv.org/abs/2203.02155
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    - TRL docs: https://huggingface.co/docs/trl/reward_trainer

skills/mlops/training/trl-fine-tuning/references/sft-training.md

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    - TRL docs: https://huggingface.co/docs/trl/sft_trainer
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    - Examples: https://github.com/huggingface/trl/tree/main/examples/scripts

skills/mlops/training/unsloth/references/llms-full.md

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    At [Unsloth](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/), our mission is to make AI as accurate and accessible as possible. Train, run, evaluate and save gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma LLMs 2
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    <a href="beginner-start-here" class="button primary">Get started</a> <a href="https://github.com/unslothai/unsloth" class="button secondary">Our GitHub</a>
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    [Credential-related source omitted]
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    {% column %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
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    {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) {% endcontent-ref %}
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    [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
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    {% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks)
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    [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks)
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    {% column %} {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models)
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    {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) {% endcontent-ref %}
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    [all-our-models](https://docs.unsloth.ai/get-started/all-our-models)
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    {% content-ref url="../models/tutorials-how-to-fine-tune-and-run-llms" %} [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms)
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    [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms)
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    [Credential-related source omitted]
  • Network accessline 40
    * Unsloth streamlines model training locally and on Colab/Kaggle, covering loading, quantization, training, evaluation, saving, exporting, and integration with inference engines like Ollama, llama.cpp, and vLLM. * We directly collaborate wi
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    * We directly collaborate with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_con
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    * Unsloth is the only training framework to support all model types:  [vision](https://docs.unsloth.ai/basics/vision-fine-tuning), [text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), BERT, [reinforcement l
  • Network accessline 46
    * Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **8-bit** training. * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speec
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    * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-n
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    * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more. Any model that works in transform
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    * **0% loss in accuracy** - no approximation methods - all exact. * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! * Unsloth supports Linux, Windows, Colab, Kagg
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    * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming!
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    * Unsloth supports Linux, Windows, Colab, Kaggle, **NVIDIA** and [**AMD**](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) & **Intel**. See:
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    {% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
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    [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
  • Network accessline 65
    Use our official **Docker image**: `unsloth/unsloth`. Read our [**Docker guide**](https://docs.unsloth.ai/get-started/install-and-update/docker)**.**
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    For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/install-and-update/windows-installation).
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    {% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    [**Fine-tuning** an LLM](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) customizes its behavior, enhances domain knowledge, and optimizes performance for specific tasks. By fine-tuning a pre-trained model (e.g. Llama-3.1-8B) on
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    [**Reinforcement Learning (RL)**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) is where an "agent" learns to make decisions by interacting with an environment and receiving **feedback** in the form of **rewards** or
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    {% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    {% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide)
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    [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide)
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    [Credential-related source omitted]
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    If you're a beginner, here might be the first questions you'll ask before your first fine-tune. You can also always ask our community by joining our [Reddit page](https://www.reddit.com/r/unsloth/).
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    [Credential-related source omitted]
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    * **Operating System**: Works on Linux and Windows. * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.u
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    * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-sp
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    Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20 & 50, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
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    * The official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker Hub.
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    * Unsloth works on [AMD](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) and [Intel](https://github.com/unslothai/unsloth/pull/2621) GPUs! Apple/Silicon/MLX is in the works.
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    **For context length benchmarks, see** [**here**](https://docs.unsloth.ai/basics/unsloth-benchmarks#context-length-benchmarks)**.**
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    **Not necessarily.** Many assume RAG will consistently outperform a fine-tuned model, but that’s not the case when fine-tuning is done properly. In fact, a well-tuned model often matches or even surpasses RAG-based systems on specialized ta
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    **Not at all!** While full fine-tuning or pretraining can be costly, these are not necessary (pretraining is especially not necessary). In most cases, LoRA or QLoRA fine-tuning can be done for minimal cost. In fact, with Unsloth’s [free not
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    We recommend starting with **QLoRA**, as it’s one of the most efficient and accessible methods available. Thanks to Unsloth’s [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss compared to standard 16-bit LoRA f
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    {% content-ref url="../fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    Also see our GitHub repo for our notebooks: [github.com/unslothai/notebooks](https://github.com/unslothai/notebooks/)
  • Network accessline 270
    * [**gpt-oss (20b)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) • [Inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)
  • Network accessline 271
    * [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new**
  • Network accessline 272
    * [Qwen3 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) • [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL
  • Network accessline 273
    * [**Qwen3-2507-4B**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) • [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) • [Instruct](https://colab.r
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    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversation
  • Network accessline 275
    * [IBM Granite-4.0-H](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) - new
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    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Vision](https://colab
  • Network accessline 277
    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)&#x20;
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    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Alpaca.ipynb) • [Llama 3.2 (1B + 3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3
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    * [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) (automatic kernels creation) - new
  • Network accessline 283
    * [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) (auto win 2048 game) - new
  • Network accessline 284
    * [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision **GSPO** - new
  • Network accessline 285
    * [Qwen3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA
  • Network accessline 286
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 287
    * [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecase)
  • Network accessline 288
    * [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)
  • Network accessline 289
    * [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA
  • Network accessline 290
    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb)
  • Network accessline 291
    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)&#x20;
  • Network accessline 292
    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)
  • Network accessline 296
    * [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) - new
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    * [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb)
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    * [Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Speech-to-Text (STT)
  • Network accessline 299
    * [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb)
  • Network accessline 300
    * [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb)
  • Network accessline 301
    * [Oute-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_\(1B\).ipynb)
  • Network accessline 305
    * [Whisper-Large-V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb)
  • Network accessline 306
    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - Audio
  • Network accessline 310
    * [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) **- new**
  • Network accessline 311
    * [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new**
  • Network accessline 312
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - vision
  • Network accessline 313
    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - vision
  • Network accessline 314
    * [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb)
  • Network accessline 315
    * [Qwen2.5-VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb)
  • Network accessline 316
    * [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb)
  • Network accessline 317
    * [Qwen3-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 318
    * [Qwen2.5-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) - Vision GSPO
  • Network accessline 319
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 329
    * [gpt-oss-120b ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(120B\)_A100-Fine-tuning.ipynb)- new
  • Network accessline 330
    * [Qwen3 (32B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(32B\)_A100-Reasoning-Conversational.ipynb) - new
  • Network accessline 331
    * [Llama 3.3 (70B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.3_\(70B\)_A100-Conversational.ipynb) - new
  • Network accessline 332
    * [Gemma 3 (27B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(27B\)_A100-Conversational.ipynb) - new
  • Network accessline 336
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 337
    * [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new**
  • Network accessline 338
    * [**ModernBERT-large**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) **- new** as of Aug 19
  • Network accessline 339
    * [**Synthetic Data Generation Llama 3.2 (3B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_\(3B\).ipynb) - new
  • Network accessline 340
    * [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new**
  • Network accessline 341
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 342
    * [Mistral v0.3 Instruct (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
  • Network accessline 343
    * [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 344
    * [ORPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb)
  • Network accessline 345
    * [Continued Pretraining](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 346
    * [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 347
    * [***Inference only***](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Inference.ipynb)
  • Network accessline 348
    * [Llama 3 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Alpaca.ipynb)
  • Network accessline 352
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 353
    * [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new**
  • Network accessline 354
    * [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 355
    * [**BERT - Text Classification**](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) **- new as of Aug 19**
  • Network accessline 356
    * [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 357
    * [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new**
  • Network accessline 358
    * [Continued Pretraining (CPT)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 359
    * [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail
  • Network accessline 360
    * [KTO](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) by Jeffrey
  • Network accessline 361
    * [Inference chat UI](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)
  • Network accessline 362
    * [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 363
    * [ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
  • Network accessline 364
    * [Text Completion](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb)
  • Network accessline 368
    * [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb)
  • Network accessline 369
    * [Gemma 2 (9B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(9B\)-Alpaca.ipynb)
  • Network accessline 370
    * [Mistral NeMo (12B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Nemo_\(12B\)-Alpaca.ipynb)
  • Network accessline 371
    * [Phi-3.5 (mini)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb)
  • Network accessline 372
    * [Phi-3 (medium)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3_Medium-Conversational.ipynb)
  • Network accessline 373
    * [Gemma 2 (2B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(2B\)-Alpaca.ipynb)
  • Network accessline 374
    * [Qwen 2.5 Coder (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(14B\)-Conversational.ipynb)
  • Network accessline 375
    * [Mistral Small (22B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_\(22B\)-Alpaca.ipynb)
  • Network accessline 376
    * [TinyLlama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/TinyLlama_\(1.1B\)-Alpaca.ipynb)
  • Network accessline 377
    * [CodeGemma (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_\(7B\)-Conversational.ipynb)
  • Network accessline 378
    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Alpaca.ipynb)
  • Network accessline 379
    * [Qwen2 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_\(7B\)-Alpaca.ipynb)
  • Network accessline 385
    * [**gpt-oss (20B)**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-gpt-oss-\(20B\)-Fine-tuning.ipynb\&accelerator=nvidiaTeslaT4) **- new**
  • Network accessline 386
    * [Gemma 3n (E4B)](https://www.kaggle.com/code/danielhanchen/gemma-3n-4b-multimodal-finetuning-inference)
  • Network accessline 387
    * [Qwen3 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(14B\).ipynb)
  • Network accessline 388
    * [Magistral-2509 (24B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=nvidiaTeslaT4) - new
  • Network accessline 389
    * [Gemma 3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(4B\).ipynb)
  • Network accessline 390
    * [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4-Conversational.ipynb)
  • Network accessline 391
    * [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Alpaca.ipynb)
  • Network accessline 392
    * [Llama 3.2 (1B + 3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 393
    * [Qwen 2.5 (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(7B\)-Alpaca.ipynb)
  • Network accessline 397
    * [**Qwen2.5-VL**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_5_7B_VL_GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Vision GRPO - new
  • Network accessline 398
    * [Qwen3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(4B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4)
  • Network accessline 399
    * [Gemma 3 (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(1B\)-GRPO.ipynb)
  • Network accessline 400
    * [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-GRPO.ipynb)
  • Network accessline 401
    * [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4_\(14B\)-GRPO.ipynb)
  • Network accessline 402
    * [Qwen 2.5 (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(3B\)-GRPO.ipynb)
  • Network accessline 406
    * [Sesame-CSM (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Sesame_CSM_\(1B\)-TTS.ipynb)
  • Network accessline 407
    * [Orpheus-TTS (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Orpheus_\(3B\)-TTS.ipynb)
  • Network accessline 408
    * [Whisper Large V3](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Whisper.ipynb) – Speech-to-Text
  • Network accessline 409
    * [Llasa-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llasa_TTS_\(1B\).ipynb)
  • Network accessline 410
    * [Spark-TTS (0.5B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Spark_TTS_\(0_5B\).ipynb)
  • Network accessline 411
    * [Oute-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Oute_TTS_\(1B\).ipynb)
  • Network accessline 415
    * [Llama 3.2 Vision (11B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(11B\)-Vision.ipynb)
  • Network accessline 416
    * [Qwen 2.5-VL (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_VL_\(7B\)-Vision.ipynb)
  • Network accessline 417
    * [Pixtral (12B) 2409](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Pixtral_\(12B\)-Vision.ipynb)
  • Network accessline 421
    * [Tool Calling](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb\&accelerator=nvidiaTeslaT4)
  • Network accessline 422
    * [ORPO](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-ORPO.ipynb)
  • Network accessline 423
    * [Continued Pretraining](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 424
    * [DPO Zephyr](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 425
    * [Inference only](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Inference.ipynb)
  • Network accessline 426
    * [Ollama](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 427
    * [Text Completion](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_\(7B\)-Text_Completion.ipynb)
  • Network accessline 428
    * [CodeForces-cot (Reasoning)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb)
  • Network accessline 429
    * [Unsloth Studio (chat UI)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Unsloth_Studio.ipynb)
  • Network accessline 433
    * [Gemma 2 (9B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(9B\)-Alpaca.ipynb)
  • Network accessline 434
    * [Gemma 2 (2B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(2B\)-Alpaca.ipynb)
  • Network accessline 435
    * [CodeGemma (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_\(7B\)-Conversational.ipynb)
  • Network accessline 436
    * [Mistral NeMo (12B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Nemo_\(12B\)-Alpaca.ipynb)
  • Network accessline 437
    * [Mistral Small (22B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Small_\(22B\)-Alpaca.ipynb)
  • Network accessline 438
    * [TinyLlama (1.1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-TinyLlama_\(1.1B\)-Alpaca.ipynb)
  • Network accessline 440
    To view a complete list of all our Kaggle notebooks, [click here](https://github.com/unslothai/notebooks#-kaggle-notebooks).
  • Network accessline 442
    {% hint style="info" %} Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)!
  • Network accessline 443
    Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)!
  • Network accessline 449
    Unsloth model catalog for all our [Dynamic](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) GGUF, 4-bit, 16-bit models on Hugging Face.
  • Network accessline 459
    | Model                                                                                      | Variant                | GGUF                                                                            | Instruct (4-bit)
  • Network accessline 460
    | ------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------- | --------------------------------------
  • Network accessline 461
    | [**gpt-oss** ](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune)            | 120b                   | [link](https://huggingface.co/unsloth/gpt-oss-120b-GGUF)                        | [link](https://huggingface.co/unsloth/
  • Network accessline 462
    |                                                                                            | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b-GGUF)                         | [link](https://huggingface.co/unsloth/
  • Network accessline 463
    | [**DeepSeek-V3.1**](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally)       | Terminus               | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)              | —
  • Network accessline 464
    |                                                                                            | V3.1                   | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF)                       | —
  • Network accessline 465
    | [**Qwen3-VL**](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune)           | 2B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 466
    |                                                                                            | 2B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 467
    |                                                                                            | 4B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 468
    |                                                                                            | 4B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 469
    |                                                                                            | 8B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 470
    |                                                                                            | 8B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 471
    |                                                                                            | 30B-A3B-Instruct       | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Instruct-GGUF)           | —
  • Network accessline 472
    |                                                                                            | 30B-A3B-Thinking       | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF)           | —
  • Network accessline 473
    |                                                                                            | 32B-Instruct           | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Instruct-GGUF)               | [link](https://huggingface.co/unsloth/
  • Network accessline 474
    |                                                                                            | 32B-Thinking           | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Thinking-GGUF)               | [link](https://huggingface.co/unsloth/
  • Network accessline 475
    |                                                                                            | 235B-A22B-Instruct     | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Instruct-GGUF)         | —
  • Network accessline 476
    |                                                                                            | 235B-A22B-Thinking     | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Thinking-GGUF)         | —
  • Network accessline 477
    | [**Qwen3-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) | 30B-A3B-Instruct       | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF)         | —
  • Network accessline 478
    |                                                                                            | 30B-A3B-Thinking       | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF)         | —
  • Network accessline 479
    |                                                                                            | 235B-A22B-Thinking     | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF/)      | —
  • Network accessline 480
    |                                                                                            | 235B-A22B-Instruct     | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF/)      | —
  • Network accessline 481
    | **Qwen3-Coder**                                                                            | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF)        | —
  • Network accessline 482
    |                                                                                            | 480B-A35B              | [link](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF)      | —
  • Network accessline 483
    | **Granite-4.0 (new)**                                                                      | H-Small                | [link](https://huggingface.co/unsloth/granite-4.0-h-small-GGUF)                 | [link](https://huggingface.co/unsloth/
  • Network accessline 484
    | **GLM (new)**                                                                              | 4.6                    | [link](https://huggingface.co/unsloth/GLM-4.6-GGUF)                             | —
  • Network accessline 485
    |                                                                                            | 4.5-Air                | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF)                         | —
  • Network accessline 486
    | **Kimi-K2-0905**                                                                           | 1T                     | [link](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF)               | —
  • Network accessline 487
    | **Gemma 3n**                                                                               | E2B                    | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 488
    |                                                                                            | E4B                    | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 489
    | **DeepSeek-R1-0528**                                                                       | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)           | [link](https://huggingface.co/unsloth/
  • Network accessline 490
    |                                                                                            | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF)                    | —
  • Network accessline 491
    | **Mistral**                                                                                | Magistral Small (2509) | [link](https://huggingface.co/unsloth/Magistral-Small-2509-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 492
    |                                                                                            | Magistral Small (2507) | [link](https://huggingface.co/unsloth/Magistral-Small-2507-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 493
    |                                                                                            | Small 3.2 24B (2506)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF) | [link](https://huggingface.co/unsloth/
  • Network accessline 494
    | FLUX.1                                                                                     | Kontext-dev            | [link](https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF)                  | —
  • Network accessline 495
    | **Qwen3**                                                                                  | 0.6 B                  | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF)                          | [link](https://huggingface.co/unsloth/
  • Network accessline 496
    |                                                                                            | 1.7 B                  | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF)                          | [link](https://huggingface.co/unsloth/
  • Network accessline 497
    |                                                                                            | 4 B                    | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF)                            | [link](https://huggingface.co/unsloth/
  • Network accessline 498
    |                                                                                            | 8 B                    | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF)                            | [link](https://huggingface.co/unsloth/
  • Network accessline 499
    |                                                                                            | 14 B                   | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF)                           | [link](https://huggingface.co/unsloth/
  • Network accessline 500
    |                                                                                            | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF)                       | [link](https://huggingface.co/unsloth/
  • Network accessline 501
    |                                                                                            | 32 B                   | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF)                           | [link](https://huggingface.co/unsloth/
  • Network accessline 502
    |                                                                                            | 235B-A22B              | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF)                     | —
  • Network accessline 503
    | **Llama 4**                                                                                | Scout 17B 16E          | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF)      | [link](https://huggingface.co/unsloth/
  • Network accessline 504
    |                                                                                            | Maverick 17B 128E      | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF)  | —
  • Network accessline 505
    | **Grok 2**                                                                                 | 270B                   | [link](https://huggingface.co/unsloth/grok-2-GGUF)                              | —
  • Network accessline 506
    | **Qwen-2.5 Omni**                                                                          | 3 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF)                     | —
  • Network accessline 507
    |                                                                                            | 7 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF)                     | —
  • Network accessline 508
    | **Phi-4**                                                                                  | Reasoning-plus         | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 509
    |                                                                                            | Reasoning              | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 513
    | Model             | Variant                | GGUF                                                                      | Instruct (4-bit)                                                                      | | ----------------- | -------
  • Network accessline 514
    | ----------------- | ---------------------- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | **DeepSeek-V3.1** | Terminu
  • Network accessline 515
    | **DeepSeek-V3.1** | Terminus               | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)        |                                                                                       |
  • Network accessline 516
    |                   | V3.1                   | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF)                 |                                                                                       |
  • Network accessline 517
    | **DeepSeek-V3**   | V3-0324                | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF)              | —                                                                                     |
  • Network accessline 518
    |                   | V3                     | [link](https://huggingface.co/unsloth/DeepSeek-V3-GGUF)                   | —                                                                                     |
  • Network accessline 519
    | **DeepSeek-R1**   | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF)              | —                                                                                     |
  • Network accessline 520
    |                   | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)     | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit)     |
  • Network accessline 521
    |                   | R1                     | [link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF)                   | —                                                                                     |
  • Network accessline 522
    |                   | R1 Zero                | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero-GGUF)              | —                                                                                     |
  • Network accessline 523
    |                   | Distill Llama 3 8 B    | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit)  |
  • Network accessline 524
    |                   | Distill Llama 3.3 70 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-bnb-4bit)         |
  • Network accessline 525
    |                   | Distill Qwen 2.5 1.5 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit) |
  • Network accessline 526
    |                   | Distill Qwen 2.5 7 B   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF)   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit)   |
  • Network accessline 527
    |                   | Distill Qwen 2.5 14 B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit)  |
  • Network accessline 528
    |                   | Distill Qwen 2.5 32 B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit)          |
  • Network accessline 532
    | Model         | Variant             | GGUF                                                                           | Instruct (4-bit)                                                                       | | ------------- | ------------
  • Network accessline 533
    | ------------- | ------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | **Llama 4**   | Scout 17 B-1
  • Network accessline 534
    | **Llama 4**   | Scout 17 B-16 E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF)     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) |
  • Network accessline 535
    |               | Maverick 17 B-128 E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | —                                                                                      |
  • Network accessline 536
    | **Llama 3.3** | 70 B                | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF)             | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit)                 |
  • Network accessline 537
    | **Llama 3.2** | 1 B                 | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)                  |
  • Network accessline 538
    |               | 3 B                 | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit)                  |
  • Network accessline 539
    |               | 11 B Vision         | —                                                                              | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 540
    |               | 90 B Vision         | —                                                                              | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit)          |
  • Network accessline 541
    | **Llama 3.1** | 8 B                 | [link](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit)             |
  • Network accessline 542
    |               | 70 B                | —                                                                              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit)            |
  • Network accessline 543
    |               | 405 B               | —                                                                              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct-bnb-4bit)           |
  • Network accessline 544
    | **Llama 3**   | 8 B                 | —                                                                              | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit)                    |
  • Network accessline 545
    |               | 70 B                | —                                                                              | [link](https://huggingface.co/unsloth/llama-3-70b-bnb-4bit)                            |
  • Network accessline 546
    | **Llama 2**   | 7 B                 | —                                                                              | [link](https://huggingface.co/unsloth/llama-2-7b-chat-bnb-4bit)                        |
  • Network accessline 547
    |               | 13 B                | —                                                                              | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit)                            |
  • Network accessline 548
    | **CodeLlama** | 7 B                 | —                                                                              | [link](https://huggingface.co/unsloth/codellama-7b-bnb-4bit)                           |
  • Network accessline 549
    |               | 13 B                | —                                                                              | [link](https://huggingface.co/unsloth/codellama-13b-bnb-4bit)                          |
  • Network accessline 550
    |               | 34 B                | —                                                                              | [link](https://huggingface.co/unsloth/codellama-34b-bnb-4bit)                          |
  • Network accessline 554
    | Model        | Variant       | GGUF                                                         | Instruct (4-bit)                                                             | | ------------ | ------------- | --------------------------------
  • Network accessline 555
    | ------------ | ------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------- | | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/u
  • Network accessline 556
    | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit)      |
  • Possible obfuscationline 556
    | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit)      |
  • Network accessline 557
    |              | E4B           | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF)  | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit)      |
  • Network accessline 558
    | **Gemma 3**  | 270M          | [link](https://huggingface.co/unsloth/gemma-3-270m-it-GGUF)  | [link](https://huggingface.co/unsloth/gemma-3-270m-it)                       |
  • Network accessline 559
    |              | 1 B           | [link](https://huggingface.co/unsloth/gemma-3-1b-it-GGUF)    | [link](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit)        |
  • Network accessline 560
    |              | 4 B           | [link](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF)    | [link](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit)        |
  • Network accessline 561
    |              | 12 B          | [link](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF)   | [link](https://huggingface.co/unsloth/gemma-3-12b-it-unsloth-bnb-4bit)       |
  • Network accessline 562
    |              | 27 B          | [link](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF)   | [link](https://huggingface.co/unsloth/gemma-3-27b-it-unsloth-bnb-4bit)       |
  • Network accessline 563
    | **MedGemma** | 4 B (vision)  | [link](https://huggingface.co/unsloth/medgemma-4b-it-GGUF)   | [link](https://huggingface.co/unsloth/medgemma-4b-it-unsloth-bnb-4bit)       |
  • Network accessline 564
    |              | 27 B (vision) | [link](https://huggingface.co/unsloth/medgemma-27b-it-GGUF)  | [link](https://huggingface.co/unsloth/medgemma-27b-text-it-unsloth-bnb-4bit) |
  • Network accessline 565
    | **Gemma 2**  | 2 B           | [link](https://huggingface.co/unsloth/gemma-2-it-GGUF)       | [link](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit)                |
  • Network accessline 566
    |              | 9 B           | —                                                            | [link](https://huggingface.co/unsloth/gemma-2-9b-it-bnb-4bit)                |
  • Network accessline 567
    |              | 27 B          | —                                                            | [link](https://huggingface.co/unsloth/gemma-2-27b-it-bnb-4bit)               |
  • Network accessline 571
    | Model                      | Variant    | GGUF                                                                         | Instruct (4-bit)                                                                | | -------------------------- | ----
  • Network accessline 572
    | -------------------------- | ---------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | | **Qwen 3**                 | 0.6
  • Network accessline 573
    | **Qwen 3**                 | 0.6 B      | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF)                       | [link](https://huggingface.co/unsloth/Qwen3-0.6B-unsloth-bnb-4bit)              |
  • Network accessline 574
    |                            | 1.7 B      | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF)                       | [link](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit)              |
  • Network accessline 575
    |                            | 4 B        | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF)                         | [link](https://huggingface.co/unsloth/Qwen3-4B-unsloth-bnb-4bit)                |
  • Network accessline 576
    |                            | 8 B        | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF)                         | [link](https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit)                |
  • Network accessline 577
    |                            | 14 B       | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF)                        | [link](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit)               |
  • Network accessline 578
    |                            | 30 B-A3B   | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF)                    | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-bnb-4bit)                   |
  • Network accessline 579
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF)                        | [link](https://huggingface.co/unsloth/Qwen3-32B-unsloth-bnb-4bit)               |
  • Network accessline 580
    |                            | 235 B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF)                  | —                                                                               |
  • Network accessline 581
    | **Qwen 2.5 Omni**          | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF)                  | —                                                                               |
  • Network accessline 582
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF)                  | —                                                                               |
  • Network accessline 583
    | **Qwen 2.5 VL**            | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF)           | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 584
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF)           | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 585
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-GGUF)          | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit) |
  • Network accessline 586
    |                            | 72 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-GGUF)          | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-unsloth-bnb-4bit) |
  • Network accessline 587
    | **Qwen 2.5**               | 0.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit)           |
  • Network accessline 588
    |                            | 1.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit)           |
  • Network accessline 589
    |                            | 3 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit)             |
  • Network accessline 590
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-bnb-4bit)             |
  • Network accessline 591
    |                            | 14 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct-bnb-4bit)            |
  • Network accessline 592
    |                            | 32 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct-bnb-4bit)            |
  • Network accessline 593
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct-bnb-4bit)            |
  • Network accessline 594
    | **Qwen 2.5 Coder (128 K)** | 0.5 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit)     |
  • Network accessline 595
    |                            | 1.5 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit)     |
  • Network accessline 596
    |                            | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K-GGUF)   | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit)       |
  • Network accessline 597
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K-GGUF)   | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit)       |
  • Network accessline 598
    |                            | 14 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF)  | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit)      |
  • Network accessline 599
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K-GGUF)  | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit)      |
  • Network accessline 600
    | **QwQ**                    | 32 B       | [link](https://huggingface.co/unsloth/QwQ-32B-GGUF)                          | [link](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit)                 |
  • Network accessline 601
    | **QVQ (preview)**          | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/QVQ-72B-Preview-bnb-4bit)                 |
  • Network accessline 602
    | **Qwen 2 (chat)**          | 1.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct-bnb-4bit)             |
  • Network accessline 603
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct-bnb-4bit)               |
  • Network accessline 604
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct-bnb-4bit)              |
  • Network accessline 605
    | **Qwen 2 VL**              | 2 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit)    |
  • Network accessline 606
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit)    |
  • Network accessline 607
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct-bnb-4bit)           |
  • Network accessline 611
    <table><thead><tr><th width="174">Model</th><th>Variant</th><th>GGUF</th><th>Instruct (4-bit)</th></tr></thead><tbody><tr><td><strong>Mistral Small</strong></td><td>3.2-24 B (2506)</td><td><a href="https://huggingface.co/unsloth/Mistral-Sma
  • Network accessline 615
    | Model       | Variant          | GGUF                                                             | Instruct (4-bit)                                                             | | ----------- | ---------------- | ------------------------
  • Network accessline 616
    | ----------- | ---------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus   | [link](https://huggingfa
  • Network accessline 617
    | **Phi-4**   | Reasoning-plus   | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-unsloth-bnb-4bit) |
  • Network accessline 618
    |             | Reasoning        | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF)      | [link](https://huggingface.co/unsloth/phi-4-reasoning-unsloth-bnb-4bit)      |
  • Network accessline 619
    |             | Mini-Reasoning   | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit) |
  • Network accessline 620
    |             | Phi-4 (instruct) | [link](https://huggingface.co/unsloth/phi-4-GGUF)                | [link](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit)                |
  • Network accessline 621
    |             | mini (instruct)  | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-GGUF)  | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit)  |
  • Network accessline 622
    | **Phi-3.5** | mini             | —                                                                | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct-bnb-4bit)        |
  • Network accessline 623
    | **Phi-3**   | mini             | —                                                                | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit)       |
  • Network accessline 624
    |             | medium           | —                                                                | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct-bnb-4bit)     |
  • Network accessline 628
    | Model          | Variant           | GGUF                                                                           | Instruct (4-bit)                                                          | | -------------- | ----------------- | -----
  • Network accessline 629
    | -------------- | ----------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | GLM            | 4.5-Air           | [link
  • Network accessline 630
    | GLM            | 4.5-Air           | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF)                        |                                                                           |
  • Network accessline 631
    |                | 4.5               | [4.5](https://huggingface.co/unsloth/GLM-4.5-GGUF)                             |                                                                           |
  • Network accessline 632
    |                | 4-32B-0414        | [4-32B-0414](https://huggingface.co/unsloth/GLM-4-32B-0414-GGUF)               |                                                                           |
  • Network accessline 633
    | Hunyuan        | A13B              | [link](https://huggingface.co/unsloth/Hunyuan-A13B-Instruct-GGUF)              | —                                                                         |
  • Network accessline 634
    | Orpheus        | 0.1-ft (3B)       | [link](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit) |
  • Network accessline 635
    | **LLava**      | 1.5 (7 B)         | —                                                                              | [link](https://huggingface.co/unsloth/llava-1.5-7b-hf-bnb-4bit)           |
  • Network accessline 636
    |                | 1.6 Mistral (7 B) | —                                                                              | [link](https://huggingface.co/unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit)  |
  • Network accessline 637
    | **TinyLlama**  | Chat              | —                                                                              | [link](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit)            |
  • Network accessline 638
    | **SmolLM 2**   | 135 M             | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-bnb-4bit)     |
  • Network accessline 639
    |                | 360 M             | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-bnb-4bit)     |
  • Network accessline 640
    |                | 1.7 B             | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-bnb-4bit)     |
  • Network accessline 641
    | **Zephyr-SFT** | 7 B               | —                                                                              | [link](https://huggingface.co/unsloth/zephyr-sft-bnb-4bit)                |
  • Network accessline 642
    | **Yi**         | 6 B (v1.5)        | —                                                                              | [link](https://huggingface.co/unsloth/Yi-1.5-6B-bnb-4bit)                 |
  • Network accessline 643
    |                | 6 B (v1.0)        | —                                                                              | [link](https://huggingface.co/unsloth/yi-6b-bnb-4bit)                     |
  • Network accessline 644
    |                | 34 B (chat)       | —                                                                              | [link](https://huggingface.co/unsloth/yi-34b-chat-bnb-4bit)               |
  • Network accessline 645
    |                | 34 B (base)       | —                                                                              | [link](https://huggingface.co/unsloth/yi-34b-bnb-4bit)                    |
  • Network accessline 653
    | Model                | Variant                | Instruct (16-bit)                                                          | | -------------------- | ---------------------- | ---------------------------------------------------------------
  • Network accessline 654
    | -------------------- | ---------------------- | -------------------------------------------------------------------------- | | **gpt-oss** (new)    | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b)
  • Network accessline 655
    | **gpt-oss** (new)    | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b)                         |
  • Network accessline 656
    |                      | 120b                   | [link](https://huggingface.co/unsloth/gpt-oss-120b)                        |
  • Network accessline 657
    | **Gemma 3n**         | E2B                    | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it)                     |
  • Network accessline 658
    |                      | E4B                    | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it)                     |
  • Network accessline 659
    | **DeepSeek-R1-0528** | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B)           |
  • Network accessline 660
    |                      | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528)                    |
  • Network accessline 661
    | **Mistral**          | Small 3.2 24B (2506)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506) |
  • Network accessline 662
    |                      | Small 3.1 24B (2503)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503) |
  • Network accessline 663
    |                      | Small 3.0 24B (2501)   | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501)     |
  • Network accessline 664
    |                      | Magistral Small (2506) | [link](https://huggingface.co/unsloth/Magistral-Small-2506)                |
  • Network accessline 665
    | **Qwen 3**           | 0.6 B                  | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                          |
  • Network accessline 666
    |                      | 1.7 B                  | [link](https://huggingface.co/unsloth/Qwen3-1.7B)                          |
  • Network accessline 667
    |                      | 4 B                    | [link](https://huggingface.co/unsloth/Qwen3-4B)                            |
  • Network accessline 668
    |                      | 8 B                    | [link](https://huggingface.co/unsloth/Qwen3-8B)                            |
  • Network accessline 669
    |                      | 14 B                   | [link](https://huggingface.co/unsloth/Qwen3-14B)                           |
  • Network accessline 670
    |                      | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B)                       |
  • Network accessline 671
    |                      | 32 B                   | [link](https://huggingface.co/unsloth/Qwen3-32B)                           |
  • Network accessline 672
    |                      | 235B-A22B              | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B)                     |
  • Network accessline 673
    | **Llama 4**          | Scout 17B-16E          | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)      |
  • Network accessline 674
    |                      | Maverick 17B-128E      | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct)  |
  • Network accessline 675
    | **Qwen 2.5 Omni**    | 3 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B)                     |
  • Network accessline 676
    |                      | 7 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B)                     |
  • Network accessline 677
    | **Phi-4**            | Reasoning-plus         | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)                |
  • Network accessline 678
    |                      | Reasoning              | [link](https://huggingface.co/unsloth/Phi-4-reasoning)                     |
  • Network accessline 682
    | Model           | Variant               | Instruct (16-bit)                                                    | | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSe
  • Network accessline 683
    | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSeek-V3** | V3-0324               | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324)              | |
  • Network accessline 684
    | **DeepSeek-V3** | V3-0324               | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324)              |
  • Network accessline 685
    |                 | V3                    | [link](https://huggingface.co/unsloth/DeepSeek-V3)                   |
  • Network accessline 686
    | **DeepSeek-R1** | R1-0528               | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528)              |
  • Network accessline 687
    |                 | R1-0528-Qwen3-8B      | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B)     |
  • Network accessline 688
    |                 | R1                    | [link](https://huggingface.co/unsloth/DeepSeek-R1)                   |
  • Network accessline 689
    |                 | R1 Zero               | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero)              |
  • Network accessline 690
    |                 | Distill Llama 3 8B    | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B)  |
  • Network accessline 691
    |                 | Distill Llama 3.3 70B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B) |
  • Network accessline 692
    |                 | Distill Qwen 2.5 1.5B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B) |
  • Network accessline 693
    |                 | Distill Qwen 2.5 7B   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B)   |
  • Network accessline 694
    |                 | Distill Qwen 2.5 14B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B)  |
  • Network accessline 695
    |                 | Distill Qwen 2.5 32B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B)  |
  • Network accessline 699
    | Family        | Variant           | Instruct (16-bit)                                                         | | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4*
  • Network accessline 700
    | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4**   | Scout 17B-16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)     | |
  • Network accessline 701
    | **Llama 4**   | Scout 17B-16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)     |
  • Network accessline 702
    |               | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) |
  • Network accessline 703
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct)             |
  • Network accessline 704
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct)              |
  • Network accessline 705
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)              |
  • Network accessline 706
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct)      |
  • Network accessline 707
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct)      |
  • Network accessline 708
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct)         |
  • Network accessline 709
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct)        |
  • Network accessline 710
    |               | 405 B             | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct)       |
  • Network accessline 711
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct)                |
  • Network accessline 712
    |               | 70 B              | [link](https://huggingface.co/unsloth/llama-3-70b-Instruct)               |
  • Network accessline 713
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b-chat)                    |
  • Network accessline 717
    | Model        | Variant | Instruct (16-bit)                                      | | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3
  • Network accessline 718
    | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | |              | E4B     | [link](https://huggingface.co/unsloth/gemma-3
  • Network accessline 719
    | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) |
  • Network accessline 720
    |              | E4B     | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) |
  • Network accessline 721
    | **Gemma 3**  | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-it)   |
  • Network accessline 722
    |              | 4 B     | [link](https://huggingface.co/unsloth/gemma-3-4b-it)   |
  • Network accessline 723
    |              | 12 B    | [link](https://huggingface.co/unsloth/gemma-3-12b-it)  |
  • Network accessline 724
    |              | 27 B    | [link](https://huggingface.co/unsloth/gemma-3-27b-it)  |
  • Network accessline 725
    | **Gemma 2**  | 2 B     | [link](https://huggingface.co/unsloth/gemma-2b-it)     |
  • Network accessline 726
    |              | 9 B     | [link](https://huggingface.co/unsloth/gemma-9b-it)     |
  • Network accessline 727
    |              | 27 B    | [link](https://huggingface.co/unsloth/gemma-27b-it)    |
  • Network accessline 731
    | Family                   | Variant   | Instruct (16-bit)                                                       | | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3
  • Network accessline 732
    | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3**               | 0.6 B     | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                       | |
  • Network accessline 733
    | **Qwen 3**               | 0.6 B     | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                       |
  • Network accessline 734
    |                          | 1.7 B     | [link](https://huggingface.co/unsloth/Qwen3-1.7B)                       |
  • Network accessline 735
    |                          | 4 B       | [link](https://huggingface.co/unsloth/Qwen3-4B)                         |
  • Network accessline 736
    |                          | 8 B       | [link](https://huggingface.co/unsloth/Qwen3-8B)                         |
  • Network accessline 737
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen3-14B)                        |
  • Network accessline 738
    |                          | 30B-A3B   | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B)                    |
  • Network accessline 739
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen3-32B)                        |
  • Network accessline 740
    |                          | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B)                  |
  • Network accessline 741
    | **Qwen 2.5 Omni**        | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B)                  |
  • Network accessline 742
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B)                  |
  • Network accessline 743
    | **Qwen 2.5 VL**          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct)           |
  • Network accessline 744
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct)           |
  • Network accessline 745
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct)          |
  • Network accessline 746
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct)          |
  • Network accessline 747
    | **Qwen 2.5**             | 0.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct)            |
  • Network accessline 748
    |                          | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct)            |
  • Network accessline 749
    |                          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct)              |
  • Network accessline 750
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct)              |
  • Network accessline 751
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct)             |
  • Network accessline 752
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct)             |
  • Network accessline 753
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct)             |
  • Network accessline 754
    | **Qwen 2.5 Coder 128 K** | 0.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K) |
  • Network accessline 755
    |                          | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K) |
  • Network accessline 756
    |                          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K)   |
  • Network accessline 757
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K)   |
  • Network accessline 758
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K)  |
  • Network accessline 759
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K)  |
  • Network accessline 760
    | **QwQ**                  | 32 B      | [link](https://huggingface.co/unsloth/QwQ-32B)                          |
  • Network accessline 761
    | **QVQ (preview)**        | 72 B      | —                                                                       | | **Qwen 2 (Chat)**        | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct)              | |
  • Network accessline 762
    | **Qwen 2 (Chat)**        | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct)              |
  • Network accessline 763
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct)                |
  • Network accessline 764
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct)               |
  • Network accessline 765
    | **Qwen 2 VL**            | 2 B       | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct)             |
  • Network accessline 766
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct)             |
  • Network accessline 767
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct)            |
  • Network accessline 771
    | Model            | Variant        | Instruct (16-bit)                                                  | | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral**      | Small
  • Network accessline 772
    | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral**      | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | | **Mistral**      | Large
  • Network accessline 773
    | **Mistral**      | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) |
  • Network accessline 774
    | **Mistral**      | Large 2407     | [link](https://huggingface.co/unsloth/Mistral-Large-Instruct-2407) |
  • Network accessline 775
    | **Mistral**      | 7B v0.3        | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3)    |
  • Network accessline 776
    | **Mistral**      | 7B v0.2        | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2)    |
  • Network accessline 777
    | **Pixtral**      | 12B 2409       | [link](https://huggingface.co/unsloth/Pixtral-12B-2409)            |
  • Network accessline 778
    | **Mixtral**      | 8×7B           | [link](https://huggingface.co/unsloth/Mixtral-8x7B-Instruct-v0.1)  |
  • Network accessline 779
    | **Mistral NeMo** | 12B 2407       | [link](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407)  |
  • Network accessline 780
    | **Devstral**     | Small 2505     | [link](https://huggingface.co/unsloth/Devstral-Small-2505)         |
  • Network accessline 784
    | Model       | Variant        | Instruct (16-bit)                                               | | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus | [link](ht
  • Network accessline 785
    | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)     | |             | Reasoning      | [link](ht
  • Network accessline 786
    | **Phi-4**   | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)     |
  • Network accessline 787
    |             | Reasoning      | [link](https://huggingface.co/unsloth/Phi-4-reasoning)          |
  • Network accessline 788
    |             | Phi-4 (core)   | [link](https://huggingface.co/unsloth/Phi-4)                    |
  • Network accessline 789
    |             | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning)     |
  • Network accessline 790
    |             | Mini           | [link](https://huggingface.co/unsloth/Phi-4-mini)               |
  • Network accessline 791
    | **Phi-3.5** | Mini           | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct)    |
  • Network accessline 792
    | **Phi-3**   | Mini           | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct)   |
  • Network accessline 793
    |             | Medium         | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) |
  • Network accessline 797
    | Model                  | Instruct (16-bit)                                                | | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft)   | [link](https://huggingfac
  • Network accessline 798
    | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft)         | | Orpheus-3B (v0.1 pt)   | [link](https://huggingfac
  • Network accessline 799
    | Orpheus-3B (v0.1 ft)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft)         |
  • Network accessline 800
    | Orpheus-3B (v0.1 pt)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) |
  • Network accessline 801
    | Sesame-CSM 1B          | [link](https://huggingface.co/unsloth/csm-1b)                    |
  • Network accessline 802
    | Whisper Large V3 (STT) | [link](https://huggingface.co/unsloth/whisper-large-v3)          |
  • Network accessline 803
    | Llasa-TTS 1B           | [link](https://huggingface.co/unsloth/Llasa-1B)                  |
  • Network accessline 804
    | Spark-TTS 0.5B         | [link](https://huggingface.co/unsloth/Spark-TTS-0.5B)            |
  • Network accessline 805
    | Oute-TTS 1B            | [link](https://huggingface.co/unsloth/Llama-OuteTTS-1.0-1B)      |
  • Network accessline 813
    | Model        | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                                           | | ------------ | ----------------- | ----------
  • Network accessline 814
    | ------------ | ----------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | **Gemma 3n** | E2B               | [link](htt
  • Network accessline 815
    | **Gemma 3n** | E2B               | [link](https://huggingface.co/unsloth/gemma-3n-E2B)              | [link](https://huggingface.co/unsloth/gemma-3n-E2B-unsloth-bnb-4bit)                   |
  • Network accessline 816
    |              | E4B               | [link](https://huggingface.co/unsloth/gemma-3n-E4B)              | [link](https://huggingface.co/unsloth/gemma-3n-E4B-unsloth-bnb-4bit)                   |
  • Network accessline 817
    | **Qwen 3**   | 0.6 B             | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base)           | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit)                |
  • Network accessline 818
    |              | 1.7 B             | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base)           | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit)                |
  • Network accessline 819
    |              | 4 B               | [link](https://huggingface.co/unsloth/Qwen3-4B-Base)             | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit)                  |
  • Network accessline 820
    |              | 8 B               | [link](https://huggingface.co/unsloth/Qwen3-8B-Base)             | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit)                  |
  • Network accessline 821
    |              | 14 B              | [link](https://huggingface.co/unsloth/Qwen3-14B-Base)            | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit)                 |
  • Network accessline 822
    |              | 30B-A3B           | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base)        | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-bnb-4bit)                     |
  • Network accessline 823
    | **Llama 4**  | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) |
  • Network accessline 824
    |              | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                                                      |
  • Network accessline 828
    | Model         | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                | | ------------- | ----------------- | -----------------------------------
  • Network accessline 829
    | ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unslo
  • Network accessline 830
    | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | —                                                           |
  • Network accessline 831
    |               | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                           |
  • Network accessline 832
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B)             | —                                                           |
  • Network accessline 833
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B)              | —                                                           |
  • Network accessline 834
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B)              | —                                                           |
  • Network accessline 835
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision)      | —                                                           |
  • Network accessline 836
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision)      | —                                                           |
  • Network accessline 837
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B)         | —                                                           |
  • Network accessline 838
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B)        | —                                                           |
  • Network accessline 839
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b)                | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)  |
  • Network accessline 840
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b)                | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit)  |
  • Network accessline 841
    |               | 13 B              | [link](https://huggingface.co/unsloth/llama-2-13b)               | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) |
  • Network accessline 845
    | Model        | Variant | Base (16-bit)                                             | Base (4-bit)                                                               | | ------------ | ------- | -------------------------------------------------
  • Network accessline 846
    | ------------ | ------- | --------------------------------------------------------- | -------------------------------------------------------------------------- | | **Qwen 3**   | 0.6 B   | [link](https://huggingface.co/unsloth/Qwen3-0.6B-
  • Network accessline 847
    | **Qwen 3**   | 0.6 B   | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base)    | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit)    |
  • Network accessline 848
    |              | 1.7 B   | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base)    | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit)    |
  • Network accessline 849
    |              | 4 B     | [link](https://huggingface.co/unsloth/Qwen3-4B-Base)      | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit)      |
  • Network accessline 850
    |              | 8 B     | [link](https://huggingface.co/unsloth/Qwen3-8B-Base)      | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit)      |
  • Network accessline 851
    |              | 14 B    | [link](https://huggingface.co/unsloth/Qwen3-14B-Base)     | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit)     |
  • Network accessline 852
    |              | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-unsloth-bnb-4bit) |
  • Network accessline 853
    | **Qwen 2.5** | 0.5 B   | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B)       | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-bnb-4bit)               |
  • Network accessline 854
    |              | 1.5 B   | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B)       | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-bnb-4bit)               |
  • Network accessline 855
    |              | 3 B     | [link](https://huggingface.co/unsloth/Qwen2.5-3B)         | [link](https://huggingface.co/unsloth/Qwen2.5-3B-bnb-4bit)                 |
  • Network accessline 856
    |              | 7 B     | [link](https://huggingface.co/unsloth/Qwen2.5-7B)         | [link](https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit)                 |
  • Network accessline 857
    |              | 14 B    | [link](https://huggingface.co/unsloth/Qwen2.5-14B)        | [link](https://huggingface.co/unsloth/Qwen2.5-14B-bnb-4bit)                |
  • Network accessline 858
    |              | 32 B    | [link](https://huggingface.co/unsloth/Qwen2.5-32B)        | [link](https://huggingface.co/unsloth/Qwen2.5-32B-bnb-4bit)                |
  • Network accessline 859
    |              | 72 B    | [link](https://huggingface.co/unsloth/Qwen2.5-72B)        | [link](https://huggingface.co/unsloth/Qwen2.5-72B-bnb-4bit)                |
  • Network accessline 860
    | **Qwen 2**   | 1.5 B   | [link](https://huggingface.co/unsloth/Qwen2-1.5B)         | [link](https://huggingface.co/unsloth/Qwen2-1.5B-bnb-4bit)                 |
  • Network accessline 861
    |              | 7 B     | [link](https://huggingface.co/unsloth/Qwen2-7B)           | [link](https://huggingface.co/unsloth/Qwen2-7B-bnb-4bit)                   |
  • Network accessline 865
    | Model         | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                | | ------------- | ----------------- | -----------------------------------
  • Network accessline 866
    | ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unslo
  • Network accessline 867
    | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | —                                                           |
  • Network accessline 868
    |               | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                           |
  • Network accessline 869
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B)             | —                                                           |
  • Network accessline 870
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B)              | —                                                           |
  • Network accessline 871
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B)              | —                                                           |
  • Network accessline 872
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision)      | —                                                           |
  • Network accessline 873
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision)      | —                                                           |
  • Network accessline 874
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B)         | —                                                           |
  • Network accessline 875
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B)        | —                                                           |
  • Network accessline 876
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b)                | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)  |
  • Network accessline 877
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b)                | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit)  |
  • Network accessline 878
    |               | 13 B              | [link](https://huggingface.co/unsloth/llama-2-13b)               | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) |
  • Network accessline 882
    | Model       | Variant | Base (16-bit)                                         | Base (4-bit)                                                           | | ----------- | ------- | ----------------------------------------------------- | ---
  • Network accessline 883
    | ----------- | ------- | ----------------------------------------------------- | ---------------------------------------------------------------------- | | **Gemma 3** | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-pt)  | [li
  • Network accessline 884
    | **Gemma 3** | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-pt)  | [link](https://huggingface.co/unsloth/gemma-3-1b-pt-unsloth-bnb-4bit)  |
  • Network accessline 885
    |             | 4 B     | [link](https://huggingface.co/unsloth/gemma-3-4b-pt)  | [link](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-bnb-4bit)  |
  • Network accessline 886
    |             | 12 B    | [link](https://huggingface.co/unsloth/gemma-3-12b-pt) | [link](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit) |
  • Network accessline 887
    |             | 27 B    | [link](https://huggingface.co/unsloth/gemma-3-27b-pt) | [link](https://huggingface.co/unsloth/gemma-3-27b-pt-unsloth-bnb-4bit) |
  • Network accessline 888
    | **Gemma 2** | 2 B     | [link](https://huggingface.co/unsloth/gemma-2-2b)     | —                                                                      |
  • Network accessline 889
    |             | 9 B     | [link](https://huggingface.co/unsloth/gemma-2-9b)     | —                                                                      |
  • Network accessline 890
    |             | 27 B    | [link](https://huggingface.co/unsloth/gemma-2-27b)    | —                                                                      |
  • Network accessline 894
    | Model       | Variant          | Base (16-bit)                                                      | Base (4-bit)                                                    | | ----------- | ---------------- | -----------------------------------
  • Network accessline 895
    | ----------- | ---------------- | ------------------------------------------------------------------ | --------------------------------------------------------------- | | **Mistral** | Small 24B 2501   | [link](https://huggingface.co/unslo
  • Network accessline 896
    | **Mistral** | Small 24B 2501   | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) | —                                                               |
  • Network accessline 897
    |             | NeMo 12B 2407    | [link](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407)      | —                                                               |
  • Network accessline 898
    |             | 7B v0.3          | [link](https://huggingface.co/unsloth/mistral-7b-v0.3)             | [link](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) |
  • Network accessline 899
    |             | 7B v0.2          | [link](https://huggingface.co/unsloth/mistral-7b-v0.2)             | [link](https://huggingface.co/unsloth/mistral-7b-v0.2-bnb-4bit) |
  • Network accessline 900
    |             | Pixtral 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-Base-2409)       | —                                                               |
  • Network accessline 904
    | Model          | Variant        | Base (16-bit)                                                    | Base (4-bit)                                                                      | | -------------- | -------------- | -----------------
  • Network accessline 905
    | -------------- | -------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **TinyLlama**  | 1.1 B (Base)   | [link](https://hu
  • Network accessline 906
    | **TinyLlama**  | 1.1 B (Base)   | [link](https://huggingface.co/unsloth/tinyllama)                 | [link](https://huggingface.co/unsloth/tinyllama-bnb-4bit)                         |
  • Network accessline 907
    | **Orpheus-3b** | 0.1-pretrained | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained-unsloth-bnb-4bit) |
  • Network accessline 916
    Unsloth works on Linux, Windows, NVIDIA, AMD, Google Colab and more. See our [system requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements).
  • Network accessline 939
    <pre class="language-bash"><code class="lang-bash">pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 940
    <strong>pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
  • Network accessline 949
    '2025.1.5' is one of the previous old versions of Unsloth. Change it to a specific release listed on our [Github here](https://github.com/unslothai/unsloth/releases).
  • Network accessline 967
    pip uninstall unsloth unsloth_zoo -y && pip install --no-deps git+https://github.com/unslothai/unsloth_zoo.git && pip install --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 981
    pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 982
    pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
  • Network accessline 989
    {% hint style="warning" %} Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install).
  • Network accessline 990
    Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install).
  • Network accessline 1000
    pip install --upgrade pip pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1001
    pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1007
    pip install --upgrade pip pip install "unsloth[cu124-torch250] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1008
    pip install "unsloth[cu124-torch250] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1014
    pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1015
    pip install "unsloth[cu118-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1016
    pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1017
    pip install "unsloth[cu118-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1019
    pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1020
    pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1022
    pip install "unsloth[cu121-torch250] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 1023
    pip install "unsloth[cu124-ampere-torch250] @ git+https://github.com/unslothai/unsloth.git"
  • Dangerous shell operationline 1029
    wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python -
  • Network accessline 1029
    wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python -
  • Network accessline 1049
    else: raise RuntimeError(f"Torch = {v} too new!") x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "") print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"')
  • Network accessline 1051
    print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"')
  • Network accessline 1061
    Unsloth Docker image: [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth)
  • Network accessline 1074
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\
  • Network accessline 1075
    Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation):
  • Dangerous shell operationline 1078
    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
  • Network accessline 1085
    [Credential-related source omitted]
  • Network accessline 1092
    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate one needed.
  • Network accessline 1102
    [Credential-related source omitted]
  • Network accessline 1109
    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth.
  • Network accessline 1111
    [Credential-related source omitted]
  • Network accessline 1115
    [Credential-related source omitted]
  • Network accessline 1122
    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
  • Network accessline 1124
    [Credential-related source omitted]
  • Access to secretsline 1139
    [Credential-related source omitted]
  • Access to secretsline 1141
    [Credential-related source omitted]
  • Access to secretsline 1155
    [Credential-related source omitted]
  • Access to secretsline 1158
    [Credential-related source omitted]
  • Access to secretsline 1161
    [Credential-related source omitted]
  • Access to secretsline 1175
    [Credential-related source omitted]
  • Access to secretsline 1178
    [Credential-related source omitted]
  • Access to secretsline 1204
    [Credential-related source omitted]
  • Access to secretsline 1206
    [Credential-related source omitted]
  • Dangerous shell operationline 1217
    * Use `USER_PASSWORD` for sudo operations inside container
  • Network accessline 1229
    Docker might be the easiest way for Windows users to get started with Unsloth as there is no setup needed or dependency issues. [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell
  • Network accessline 1231
    For installation instructions, please follow our [Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker), otherwise here is a quickstart guide:
  • Network accessline 1238
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other). Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/ins
  • Dangerous shell operationline 1241
    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
  • Network accessline 1254
    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image.
  • Network accessline 1270
    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. Access the `unsloth-notebooks` tabs to see Unsloth notebooks.
  • Network accessline 1277
    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
  • Network accessline 1291
    You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx)
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    * Launch the Installer here:  [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/)
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    Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
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    Then install Miniconda (which has Python) here: [https://www.anaconda.com/docs/getting-started/miniconda/install](https://www.anaconda.com/docs/getting-started/miniconda/install#quickstart-install-instructions)
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    You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully. [Install PyTorch](https://pytorch.org/get-started/locally/)
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    pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git"
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    * Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
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    1. Install `torch` and `triton`. Go to <https://pytorch.org> to install it. For example `pip install torch torchvision torchaudio triton`
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    2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers. 3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succee
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    3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to <https://github.com/facebookresearch/xformers>. Another option is to install `fl
  • Network accessline 1392
    4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-m
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    1. **Install NVIDIA CUDA Toolkit**:    * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads).
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    * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads).
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    * **Note**: No additional setup is required after installation for Unsloth. 2. **Install Microsoft C++ Build Tools**:    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.mic
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    2. **Install Microsoft C++ Build Tools**:    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/).    * During installation, select the **
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    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/).
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    * Verify: Open a new terminal and type `cl`. It should show version info. 4. **Install Conda**    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
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    4. **Install Conda**    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)    2. Follow installation instruction from the website
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    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
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    1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**.
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    1. Install python though [Python's official site](https://www.python.org/downloads/windows/).
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    sudo apt update && sudo apt upgrade -y
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    sudo apt install python3-pip
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    Install the latest PyTorch, TorchAO, Xformers from <https://pytorch.org/>
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    pip install --upgrade torch==2.8.0 pytorch-triton-rocm torchvision torchaudio torchao==0.13.0 xformers --index-url https://download.pytorch.org/whl/rocm6.4
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    pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git
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    pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git
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    pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth"
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    And that's it! Try some examples in our [**Unsloth Notebooks**](https://docs.unsloth.ai/get-started/unsloth-notebooks) page!
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    You can use our :ledger:[gpt-oss RL auto win 2048](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game_BF16.ipynb) example on a MI300X (192GB) GPU. The goal is to play the 2048 game automatic
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    You can also use our :ledger:[automatic kernel gen RL notebook](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_GRPO_BF16.ipynb) also with gpt-oss to auto create matrix multiplication kernels in Python. The notebook also
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    [Credential-related source omitted]
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    AMD GPUs also need the bitsandbytes `blocksize` to be 128 and not 64 - this also means our pre-quantized models (for example [unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)) fr
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    {% hint style="warning" %} Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install).
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    Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install).
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    If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below:
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    mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
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    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
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    rm -rf ~/miniconda3/miniconda.sh
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any! # Get LAION dataset url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
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    # Get LAION dataset url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
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    url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
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    ] # More models at https://huggingface.co/unsloth
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    # Go to https://docs.unsloth.ai for advanced tips like
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    * <U+200B>**DeepSeek-R1-Distill-Llama-8B** is a fine-tuned version of Llama-3.1-8B. DeepSeek utilized data generated by DeepSeek-R1, to fine-tune Llama-3.1-8B. This process, known as distillation (a subcategory of fine-tuning), injects the data in
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    With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune for free on Colab, Kaggle, or locally with just 3GB VRAM by using our [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). By fine-tuning a pre-trained m
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    You may have heard that fine-tuning does not make a model learn new knowledge or RAG performs better than fine-tuning. That is **false**. Read more FAQ + misconceptions [here](https://docs.unsloth.ai/beginner-start-here/faq-+-is-fine-tuning
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    {% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    We recommend starting with **Instruct models**, as they allow direct fine-tuning using conversational chat templates (ChatML, ShareGPT etc.) and require less data compared to **Base models** (which uses Alpaca, Vicuna etc). Learn more about
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    * Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi
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    We recommend starting with QLoRA, as it is one of the most accessible and effective methods for training models. Our [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss for QLoRA compared to LoRA is now largely r
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    You can also do [Text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [reasoning (GRPO)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [vision](https://docs.unsloth.ai/basics/vision-f
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    {% content-ref url="fine-tuning-llms-guide/what-model-should-i-use" %} [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use)
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    [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use)
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    * You will need to create a dataset usually with 2 columns - question and answer. The quality and amount will largely reflect the end result of your fine-tune so it's imperative to get this part right. * You can [synthetically generate data
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    * You can [synthetically generate data](https://docs.unsloth.ai/get-started/datasets-guide#synthetic-data-generation) and structure your dataset (into QA pairs) using ChatGPT or local LLMs.
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    * You can also use our new Synthetic Dataset notebook which automatically parses documents (PDFs, videos etc.), generates QA pairs and auto cleans data using local models like Llama 3.2. [Access the notebook here.](https://colab.research.go
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    {% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
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    [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
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    For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which
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    Learn how to choose the right [hyperparameters](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) using best practices from research and real-world experiments - and understand how each one affects your
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    {% content-ref url="fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    We would recommend beginners to utilise our pre-made [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) first as it's the easiest way to get started with guided steps. However, if installing locally is a must, you can instal
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    {% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
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    [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
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    {% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    [Credential-related source omitted]
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    For testing, you can also  take 20% of your training data and use that for testing. If you already used all of the training data, then you have to manually evaluate it. You can also use automatic eval tools like EleutherAI’s [lm-evaluation-
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    {% content-ref url="../basics/running-and-saving-models" %} [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models)
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    [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models)
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    We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingfac
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    To learn more about fine-tuning tips and tricks, head over to our blogs which provide tremendous and educational value: <https://unsloth.ai/blog/>
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    If you need any help on fine-tuning, you can also join our Discord server [here](https://discord.gg/unsloth) or [Reddit r/unsloth](https://www.reddit.com/r/unsloth/). Thanks for reading and hopefully this was helpful!
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    * E.g. For image-based training, select a vision model such as *Llama 3.2 Vision*. For code datasets, opt for a specialized model like *Qwen Coder 2.5*. * **Licensing and Requirements**: Different models may have specific licensing terms an
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    * **Licensing and Requirements**: Different models may have specific licensing terms and [system requirements](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#system-requirements). Be sure to review these carefully to avoid
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    * Use our [VRAM guideline](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#approximate-vram-requirements-based-on-model-parameters) to determine the VRAM requirements for the model you’re considering.
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    * We recommend using the latest model for the best performance and capabilities. For instance, as of January 2025, the leading 70B model is *Llama 3.3*. * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-
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    * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-started/all-our-models) to find the newest and relevant options.
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    Base models, on the other hand, are the original pre-trained versions without instruction fine-tuning. These are specifically designed for customization through fine-tuning, allowing you to adapt them to your unique needs. Base models are c
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    * **300–1,000 Rows of High-Quality Data**: With a medium-sized, high-quality dataset, fine-tuning the base or instruct model are both viable options. * **Less than 300 Rows**: For smaller datasets, the instruct model is typically the better
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    * For information how how big your dataset should be, [see here](https://docs.unsloth.ai/get-started/datasets-guide#how-big-should-my-dataset-be)
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    * Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi
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    One of the key parts of creating a dataset is your [chat template](https://docs.unsloth.ai/basics/chat-templates) and how you are going to design it. Tokenization is also important as it breaks text into tokens, which can be words, sub-word
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    * Chat-based dialogues (Q\&A, learn a new language, customer support, conversations). * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.
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    * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb), summarization, generation tasks).
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    For [**continued pretraining**](https://docs.unsloth.ai/basics/continued-pretraining), we use raw text format without specific structure:
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    If we are adapting a model to a new task, and intend for the model to output text in a single turn based on a specific set of instructions, we can use **Instruction** format in [Alpaca style](https://docs.unsloth.ai/basics/tutorial-how-to-f
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    <mark style="color:green;">**A:**</mark>  If your dataset is already formatted in the Alpaca format, then follow the formatting steps as shown in the Llama3.1 [notebook ](https://colab.research.google.com/github/unslothai/notebooks/blob/mai
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    <mark style="color:green;">**A:**</mark>  The `chat_template` attribute when a model is first uploaded by the original model owners sometimes contains errors and may take time to be updated. In contrast, at Unsloth, we thoroughly check and
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    <mark style="color:green;">**A:**</mark>  Submit a feature request on the unsloth github issues [forum](https://github.com/unslothai/unsloth). As a temporary workaround, you could also use the tokenizer's own apply\_chat\_template function
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    * Produce entirely new data - either from scratch or from your existing dataset * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting)
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    * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) and become too specific
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    We collaborated with Meta to launch a free notebook for creating Synthetic Datasets automatically using local models like Llama 3.2. [Access the notebook here.](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_
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    If you want to fine-tune a model that already has reasoning capabilities like the distilled versions of DeepSeek-R1 (e.g. DeepSeek-R1-Distill-Llama-8B), you will need to still follow question/task and answer pairs however, for your answer y
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    For a model that does not have reasoning and you want to train it so that it later encompasses reasoning capabilities, you will need to utilize a standard dataset but this time without reasoning in its answers. This is training process is k
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    * Standardize the format of all datasets, combine them into a single dataset, and fine-tune on this unified dataset. * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebo
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    * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebook to fine-tune on multiple datasets directly.
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    [Credential-related source omitted]
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    You can access the GPT4 version of the Alpaca dataset [here](https://huggingface.co/datasets/vicgalle/alpaca-gpt4.). Below shows some examples of the dataset:
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    The dataset for fine-tuning a vision or multimodal model also includes image inputs. For example, the [Llama 3.2 Vision Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scroll
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    We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and
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    | Image
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    [Credential-related source omitted]
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    For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX).
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    * **LoRA:** 16-bit fine-tuning. It's slightly faster and slightly more accurate, but consumes significantly more VRAM (4× more than QLoRA). Recommended for 16-bit environments and scenarios where maximum accuracy is required. * **QLoRA:** 4
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    :sloth: *70B LLaMA fits in <48GB VRAM with QLoRA in Unsloth -* [*more details here*](https://unsloth.ai/blog/llama3-3)*.*
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    [Read our blog post](https://unsloth.ai/blog/gradient) for more details.
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    A regularization technique that helps [prevent overfitting](#overfitting-poor-generalization-too-specialized) by randomly setting a fraction of the LoRA activations to zero during each training step. [Recent research suggests](https://arxiv
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    Options are `True`, `False`, and `"unsloth"`. \    🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](
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    🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](https://unsloth.ai/blog/long-context)*.*\\
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    An advanced feature that implements [**Rank-Stabilized LoRA**](https://arxiv.org/abs/2312.03732). If set to `True`, the effective scaling becomes `lora_alpha / sqrt(r)` instead of the standard `lora_alpha / r`. This can sometimes improve st
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    An advanced technique, as proposed in [**LoftQ**](https://arxiv.org/abs/2310.08659), initializes LoRA matrices with the top 'r' singular vectors from the pretrained weights. This can improve accuracy but may cause a significant memory spike
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    When validating that **LoRA** adapter weights have been updated after fine-tuning, avoid using **np.allclose()** for comparison. This method can miss subtle but meaningful changes, particularly in **LoRA A**, which is initialized with small
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    According to the [rsLoRA (rank stabilized lora) paper](https://arxiv.org/abs/2312.03732), we should instead scale alpha by the sqrt of the rank. Other options exist, but theoretically this is the optimum. The left plot shows other ranks and
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    According to empirical experiments and research papers like the original [QLoRA paper](https://arxiv.org/pdf/2305.14314), it's best to apply LoRA to both attention and MLP layers.
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    The [QLoRA paper](https://arxiv.org/pdf/2305.14314) shows that masking out inputs and **training only on completions** (outputs or assistant messages) can further **increase accuracy** by a few percentage points (*1%*). Below demonstrates h
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    The QLoRA paper states that **training on completions only** increases accuracy by quite a bit, especially for multi-turn conversational finetunes! We do this in our [conversational notebooks here](https://colab.research.google.com/github/u
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    ***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!*
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    By the end of this tutorial, you will create a custom chatbot by **finetuning Llama-3** with [**Unsloth**](https://github.com/unslothai/unsloth) for free. It can run locally via [**Ollama**](https://github.com/ollama/ollama) on your PC, or
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    **Unsloth** makes finetuning much easier, and can automatically export the finetuned model to **Ollama** with integrated automatic `Modelfile` creation! If you need help, you can join our Discord server: <https://discord.com/invite/unsloth>
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    {% hint style="warning" %} **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You c
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    **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You can use it directly there or
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    [Unsloth](https://github.com/unslothai/unsloth) makes finetuning LLMs like Llama-3, Mistral, Phi-3 and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy! We will be using Google Colab which provides a free GPU during
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    * [Ollama Llama-3 Alpaca](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) (notebook which we will be using)
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    * [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing)
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    [Ollama ](https://github.com/ollama/ollama)allows you to run language models from your own computer in a quick and simple way! It quietly launches a program which can run a language model like Llama-3 in the background. If you suddenly want
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    Options include `True`, `False` and `"unsloth"`. We suggest `"unsloth"` since we reduce memory usage by an extra 30% and support extremely long context finetunes.You can read up here: <https://unsloth.ai/blog/long-context> for more details.
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    You can access the GPT4 version of the Alpaca dataset here: <https://huggingface.co/datasets/vicgalle/alpaca-gpt4>. An older first version of the dataset is here: <https://github.com/tatsu-lab/stanford_alpaca>. Below shows some examples of
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    To access the Titanic finetuning notebook or if you want to upload a CSV or Excel file, go here: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing>
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    Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab  notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin
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    We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via <https://huggingface
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    Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf>
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    If you want to learn how to do reward modelling, do continued pretraining, export to vLLM or GGUF, do text completion, or learn more about finetuning tips and tricks, head over to our [Github](https://github.com/unslothai/unsloth#-finetune-
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    If you need any help on finetuning, you can also join our Discord server [here](https://discord.gg/unsloth). If you want help with Ollama, you can also join their server [here](https://discord.gg/ollama).
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    To access our Alpaca dataset example click [here](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing), and our CSV / Excel finetuning guide is [here](https://colab.research.google.com/drive/1VYkncZMfGFkeCE
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    {% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    This article covers everything (from beginner to advanced) you need to know about GRPO, Reinforcement Learning (RL) and reward functions, along with tips, and the basics of using GRPO with [Unsloth](https://github.com/unslothai/unsloth). If
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    {% column %} OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**<
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    OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**</mark> to pro
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    {% column %} In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agen
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    In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agent**</mark> is
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    The formula for PPO looks quite complicated because it was designed to be stable. Visit our [AI Engineer talk](https://docs.unsloth.ai/ai-engineers-2025) we gave in 2025 about RL for more in depth maths derivations about PPO.
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    {% column %} DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are:
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    DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are:
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    2. Code output can be verified as having executed correctly or not. 3. Designing verifiable reward functions can be tough, and so most examples are math or code. 4. Use-cases for GRPO isn’t just for code or math—its reasoning process can en
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    4. Use-cases for GRPO isn’t just for code or math—its reasoning process can enhance tasks like email automation, database retrieval, law, and medicine, greatly improving accuracy based on your dataset and reward function - the trick is to d
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    * With 15GB VRAM, Unsloth allows you to transform any model up to 17B parameters like Llama 3.1 (8B), Phi-4 (14B), Mistral (7B) or Qwen2.5 (7B) into a reasoning model * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.un
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    * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!**
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    {% content-ref url="reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo" %} [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train
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    [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo)
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    | [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **GSPO -** new | [**Qwen3-VL-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_
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    | -------------------------------------------------------------------------------------------------------------------------------------- | -----------------------------------------------------------------------------------------------------
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    | [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced         | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/mai
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    | [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)                     | [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)
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    | [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)          | [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\
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    {% hint style="success" %} **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in
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    **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in GRPOConfig to enable:
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    For a tutorial on how to transform any open LLM into a reasoning model using Unsloth & GRPO, [see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo).
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    {% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    {% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew
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    If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should
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    In our other examples, we use existing GSM8K reward functions by [@willccbb](https://x.com/willccbb) which is popular and shown to be quite effective:
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    You can now use [vLLM](https://github.com/vllm-project/vllm/) directly in your finetuning stack, which allows for much more throughput and allows you to finetune and do inference on the model at the same time! On 1x A100 40GB, expect 4000 t
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    1. For GRPO's **GPU VRAM requirements for QLoRA 4-bit**, the general rule is the model parameters = the amount of VRAM you will need (you can use less VRAM but this just to be safe). The more context length you set, the more VRAM. LoRA 16-b
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    2. Our new memory efficient linear kernels for GRPO slashes memory usage by 8x or more. This shaves 68.5GB of memory, whilst being actually faster through the help of torch.compile! 3. We leverage our smart [Unsloth gradient checkpointing](
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    3. We leverage our smart [Unsloth gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm which we released a while ago. It smartly offloads intermediate activations to system RAM asynchronously whilst being only 1% slower.
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    {% embed url="<https://www.youtube.com/watch?v=OkEGJ5G3foU>" %}
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    1. Nathan Lambert's RLHF Book is a must! <https://rlhfbook.com/c/11-policy-gradients.html>
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    2. Yannic Kilcher's GRPO Youtube video is also a must! <https://www.youtube.com/watch?v=bAWV_yrqx4w>
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    3. We did a 3 hour workshop at AI Engineer World's Fair 2025. Slides are other material are at <https://docs.unsloth.ai/ai-engineers-2025>
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    4. Advanced GRPO notebook via Unsloth. <https://docs.unsloth.ai/basics/reinforcement-learning-guide/tutorial-train-your-own-reasoning-model-with-grpo>
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    5. GRPO from a base model notebook: <https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb>
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    DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models.
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    These instructions are for our pre-made Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). If you are installing Unsloth locally, you can also copy our notebooks inside your favorite code editor. We'll be using
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    | [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **-** GSPO | [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_
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    | [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced     | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb
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    If you're using our Colab notebook, click **Runtime > Run all**. We'd highly recommend you checking out our [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) before getting started.
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    If installing locally, ensure you have the correct [requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and use `pip install unsloth` on Linux or follow our [Windows install ](https://docs.unsloth.ai/
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    Before we get started, it is recommended to learn more about GRPO, reward functions and how they work. Read more about them including [tips & tricks](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#basics-tips)[ here]
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    We have pre-selected optimal settings for the best results for you already and you can change the model to whichever you want listed in our [supported models](https://docs.unsloth.ai/get-started/all-our-models). Would not recommend changing
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    {% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    We have pre-selected OpenAI's [GSM8K](https://huggingface.co/datasets/openai/gsm8k) dataset which contains grade school math problems but you could change it to your own or any public one on Hugging Face. You can read more about [datasets h
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    [Reward Functions/Verifiers](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#reward-functions-verifier) lets us know if the model is doing well or not according to the dataset you have provided. Each generation run wi
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    You can input your generations into an LLM like ChatGPT 4o or Llama 3.1 (8B) and design a reward function and verifier to evaluate it. For example, feed your generations into a LLM of your choice and set a rule: "If the answer sounds too ro
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    We have pre-selected hyperparameters for the most optimal results however you could change them. Read all about [parameters here](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide). For **advanced GRPO**
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    {% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew
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    If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should
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    You can then save your model to GGUF, Ollama etc. by following our [guide here](https://docs.unsloth.ai/fine-tuning-llms-guide#id-7.-running--saving-the-model).
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    We have multiple options for saving your fine-tuned model, but we’ll focus on the easiest and most popular approaches which you can read more about [here](https://docs.unsloth.ai/basics/running-and-saving-models)
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    {% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %}
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    This can zero out all `completion_mask` entries when many completions are truncated, making `n_mask_per_reward = 0` and causing KL to become NaN. [See](https://github.com/unslothai/unsloth-zoo/blob/e705f7cb50aa3470a0b6e36052c61b7486a39133/u
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    This means the memory space for inference and training can in theory be re-used, since inference and training are separate modes - this is where [vLLM's sleep mode feature](https://docs.vllm.ai/en/latest/features/sleep_mode.html#rlhf-weight
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    <table data-full-width="true"><thead><tr><th>Experiments</th><th>Config</th><th>Status</th><th>GPU Memory usage</th><th>Comments</th></tr></thead><tbody><tr><td><ol><li><a href="https://colab.research.google.com/drive/18CssBY5C0mStnLvu2Hlt4
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    We now select better compilation flags and reduce compile times by 50% or more. We also managed to dynamically patch any vLLM version to handle `gc.collect` better for backwards compatibility reasons, as inspired from this [vLLM pull reques
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    All our GRPO notebooks have Unsloth Standby on by default and all optimizations! See <https://docs.unsloth.ai/get-started/unsloth-notebooks> for all our GRPO notebooks, or try the below:
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    * [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA
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    * [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecases)
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    * [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)
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    * [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA
  • Network accessline 4355
    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb)
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    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)
  • Network accessline 4357
    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)
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    * [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb)
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    It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking).
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    <div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div>
  • Network accessline 4371
    **Can you counter reward hacking? Yes!** In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation s
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    We're introducing GSPO which is a variant of [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#from-rlhf-ppo-to-grpo-and-rlvr) made by the Qwen team at Alibaba. They noticed the observation that when GRPO takes i
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    * Use our free GSPO notebooks for: [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) and [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blo
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    * [GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-rl-notebooks)
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    * [ORPO notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb)
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    * [DPO Zephyr notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
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    * [KTO notebook](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing)
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    * [SimPO notebook](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing)
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    We're also in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#acceler
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    [Credential-related source omitted]
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    You can fine-tune DeepSeek-OCR to enhance its vision or language performance. In our Unsloth [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb), we demonstrat
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    > **Our model upload that enables fine-tuning + more inference support:** [**DeepSeek-OCR**](https://huggingface.co/unsloth/DeepSeek-OCR)
  • Network accessline 4561
    source .venv/bin/activate # Until v0.11.1 release, you need to install vLLM from nightly build uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
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    uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
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    [Credential-related source omitted]
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    Unsloth supports fine-tuning of DeepSeek-OCR. Since the default model isn’t fine-tunable, we added changes from the [Stranger Vision HF](https://huggingface.co/strangervisionhf) team, to then enable fine-tuning. As usual, Unsloth trains Dee
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    * DeepSeek-OCR: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb)
  • Network accessline 4660
    * DeepSeek-OCR: [Fine-tuning + Evaluation notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\)-Eval.ipynb) (A100)
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    [Credential-related source omitted]
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    Local training can be complex due to dependency hell or breaking environments. Unsloth’s [Docker image](https://hub.docker.com/r/unsloth/unsloth) can bypass these issues. No setup is needed: pull and run the image and start training.
  • Network accessline 4780
    * **Unsloth official Docker image:** [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth)
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    Unsloth’s Docker image is stable, up-to-date and works in [supported setups](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements#system-requirements) like Windows.
  • Network accessline 4800
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\
  • Network accessline 4801
    Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation):
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    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
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    [Credential-related source omitted]
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    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and 50-series GPUs, use this same im
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    [Credential-related source omitted]
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    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth.
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
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    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Dangerous shell operationline 4928
    * Use `USER_PASSWORD` for sudo operations inside container
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    Unsloth now supports vision/multimodal RL with [Qwen3-VL](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune), [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune) and more. Due to Unsloth's unique [weigh
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    Unsloth can train Qwen3-VL-8B with GSPO/GRPO on a free Colab T4 GPU. Other VLMs work too, but may need larger GPUs. Gemma requires newer GPUs than T4 because vLLM [restricts to Bfloat16](https://docs.unsloth.ai/models/gemma-3-how-to-run-and
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    * **Qwen-3 VL-8B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb)
  • Network accessline 4941
    * **Qwen-2.5 VL-7B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) •[ Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/note
  • Network accessline 4942
    * **Gemma-3-4B** (Unsloth inference): [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb)
  • Network accessline 4944
    We have also added vLLM VLM integration into Unsloth natively, so all you have to do to use vLLM inference is enable the `fast_inference=True` flag when initializing the model. Special thanks to [Sinoué GAD](https://github.com/unslothai/uns
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    This VLM support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), which un
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    This was [reported](https://github.com/QwenLM/Qwen2.5-VL/issues/759) as well in Qwen2.5-VL-7B-Instruct output unexpected results "addCriterion". In fact we see this as well! We tried both non Unsloth, bfloat16 and float16 machines and other
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    # Fix up addCriterion issues         # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks
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    # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks
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    This update in addition adds GSPO ([Group Sequence Policy Optimization](https://arxiv.org/abs/2507.18071)) which is a variant of GRPO made by the Qwen team at Alibaba. They noticed that GRPO implicitly results in importance weights for each
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    [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide "mention")
  • Network accessline 5107
    ***Authors:** A huge thank you to* [*Keith*](https://www.linkedin.com/in/keith-truongcao-7bb84a23b/) *and* [*Datta*](https://www.linkedin.com/in/datta0/) *for contributing to this article!*
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    You can now train OpenAI [gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) with RL and GRPO via [Unsloth](https://github.com/unslothai/unsloth). Unsloth now offers the <mark style="background-color:$success;">**fast
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    Since reinforcement learning (RL) on gpt-oss isn't yet vLLM compatible, we had to rewrite the inference code from Transformers code to deliver 3x faster inference for gpt-oss at \~21 tokens/s. For BF16, Unsloth also achieves the fastest inf
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    * **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\
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    [Credential-related source omitted]
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    Unsloth is the only framework to support 4-bit RL for gpt-oss. All performance gains are due to Unsloth's unique [weight sharing](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#what-unsloth-offers-for-rl), [Flex Attenti
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    {% hint style="warning" %} Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and
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    Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt
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    Inference is crucial in RL training, since we need it to generate candidate solutions before maximizing some reward function ([see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) for a more detailed explanation).
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    vLLM does not support RL for gpt-oss since it lacks BF16 training and LoRA support for gpt-oss. Without Unsloth, only training via full precision BF16 works, making <mark style="background-color:$warning;">memory use</mark> <mark style="bac
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    We had to change our implementation for attention sinks as [described here](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) to allow generation to work with left padding. We had to get the logs
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    The harder part is batch generation. Sequences differ in length, so padding complicates mask creation. Flex Attention had a lot of [challenges](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) and dynamic mas
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    > *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665)
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    It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking).
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    <div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div>
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    In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation setting and showcase tangible solutions to
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    LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs.
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    RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo
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    | [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo
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    LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs.
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    RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo
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    | [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo
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    !uv pip install -qqq \     "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
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    "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \     "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
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    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
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    "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
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    git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
  • Network accessline 5326
    Load the 20B model in 4‑bit QLoRA for memory efficiency, then wrap it with a LoRA adapter. You can also train it in 16-bit LoRA but it will use 4x more memory. For more settings view our [configuration guide](https://docs.unsloth.ai/get-sta
  • Network accessline 5576
    We will use the **GRPOTrainer**. Set the prompt/completion lengths, then build a `GRPOConfig`. Keep in mind you could also set the RL algorithm type to others such as [GSPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guid
  • Network accessline 5703
    We’re excited to share that Unsloth Dynamic GGUFs shows how it's possible to quantize LLMs like [DeepSeek-V3.1](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) (671B) down to just **1-bit** or **3-bit**, and still be able t
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    Previously, [we demonstrated](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) how Unsloth Dynamic GGUFs outperform other quantization methods on 5-shot MMLU and KL Divergence. Now, we’re showcasing their performance on independent
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    **Why the** [**Aider Polyglot**](https://aider.chat/docs/leaderboards/) **benchmark?** Aider is one of the most comprehensive measures of how well LLMs can write, code, follow instructions, and apply changes without human intervention, maki
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    {% hint style="success" %} The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalL
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    The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3
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    In Nov 2024, our [4-bit Dynamic](https://unsloth.ai/blog/dynamic-4bit) Quants showcased how you could largely restore QLoRA fine-tuning & model accuracy by just <mark style="background-color:green;">**selectively quantizing layers**</mark>.
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    Our Dynamic GGUFs are even more effective when paired with our [imatrix calibration dataset](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs#whats-new-in-dynamic-v2.0), designed for chat and coding performance. All of this enabled
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    We also showed dynamic benchmarks in <https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs> for Gemma 3 and Llama 4 Scout, showing how effective our methodology is:
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    Benchmark experiments were mainly conducted by [David Sluys](https://www.linkedin.com/in/david-sluys-231348208/) (neolithic5452 on [Aider Discord](https://discord.com/channels/1131200896827654144/1408293692074360914)), a trusted community c
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    See [here](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?chat_template=default\&format=true) for our fixed chat template or [here](https://huggingface.co/unsloth/DeepSeek-V3.1/raw/main/chat_template.jinja) for a raw jinja file.
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    [Credential-related source omitted]
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    Head over to our [DeepSeek V3.1 guide](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-how-to-run-locally/deepseek-r1-dynamic-1.58-bit) or to quickly get the dynamic 2bit version, do:
  • Network accessline 5877
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    Qwen3-VL has vision, video and OCR capabilities as well as 256K context (can be extended to 1M).\ \ [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcem
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    [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl). Train Qwen3-VL (8B) for free with our [notebooks](#fine-tuning-qwen3-vl).
  • Network accessline 5921
    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF">2B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF">2B-Thinking</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL
  • Network accessline 5939
    Qwen3-VL also used the below settings for their benchmarking numbers, as mentioned [on GitHub](https://github.com/QwenLM/Qwen3-VL/tree/main?tab=readme-ov-file#generation-hyperparameters).
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    [Credential-related source omitted]
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 6016
    2. **Let's first get an image!** You can also upload images as well. We shall use <https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png>, which is just our mini logo showing how finetun
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    [Credential-related source omitted]
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    wget https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png -O unsloth.png
  • Network accessline 6030
    4. Let's get the 2nd image at <https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg>
  • Network accessline 6032
    [Credential-related source omitted]
  • Network accessline 6037
    wget https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg -O picture.png
  • Network accessline 6060
    [Credential-related source omitted]
  • Network accessline 6064
    [Credential-related source omitted]
  • Network accessline 6068
    [Credential-related source omitted]
  • Network accessline 6072
    [Credential-related source omitted]
  • Network accessline 6076
    [Credential-related source omitted]
  • Network accessline 6093
    [Credential-related source omitted]
  • Access to secretsline 6100
    [Credential-related source omitted]
  • Access to secretsline 6154
    [Credential-related source omitted]
  • Network accessline 6235
    * [Normal SFT fine-tuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb)
  • Network accessline 6236
    * [GRPO/GSPO RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb)
  • Network accessline 6246
    [Credential-related source omitted]
  • Network accessline 6248
    This Qwen3-VL support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), whi
  • Network accessline 6277
    {% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM,
  • Network accessline 6278
    [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc.
  • Network accessline 6280
    We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t
  • Network accessline 6283
    > [**Fine-tune**](#fine-tuning-gpt-oss-with-unsloth) **gpt-oss-20b for free with our** [**Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb)
  • Network accessline 6285
    Trained with [RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), **gpt-oss-120b** rivals o4-mini and **gpt-oss-20b** rivals o3-mini. Both excel at function calling and CoT reasoning, surpassing o1 and GPT-4o.
  • Network accessline 6293
    * 20B: [gpt-oss-**20B**](https://huggingface.co/unsloth/gpt-oss-20b-GGUF)
  • Network accessline 6294
    * 120B: [gpt-oss-**120B**](https://huggingface.co/unsloth/gpt-oss-120b-GGUF)
  • Network accessline 6298
    OpenAI released a standalone parsing and tokenization library called [Harmony](https://github.com/openai/harmony) which allows one to tokenize conversations to OpenAI's preferred format for gpt-oss. The official OpenAI [cookbook article](ht
  • Network accessline 6302
    [Credential-related source omitted]
  • Network accessline 6347
    [Credential-related source omitted]
  • Network accessline 6353
    We made a [MXFP4 inference notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) as well in Tesla T4 Colab!
  • Network accessline 6355
    {% hint style="info" %} [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case,
  • Network accessline 6356
    [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, microscaled lhs/rhs are
  • Network accessline 6401
    [Credential-related source omitted]
  • Network accessline 6403
    To achieve inference speeds of 6+ tokens per second for our Dynamic 4-bit quant, have at least **14GB of unified memory** (combined VRAM and RAM) or **14GB of system RAM** alone. As a rule of thumb, your available memory should match or exc
  • Network accessline 6413
    > **You can run gpt-oss-20b for free with our** [**Google Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb)
  • Network accessline 6425
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 6428
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6429
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6430
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 6450
    [Credential-related source omitted]
  • Network accessline 6461
    [Credential-related source omitted]
  • Network accessline 6463
    To achieve inference speeds of 6+ tokens per second for our 1-bit quant, we recommend at least **66GB of unified memory** (combined VRAM and RAM) or **66GB of system RAM** alone. As a rule of thumb, your available memory should match or exc
  • Network accessline 6479
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 6482
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6483
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6484
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 6515
    [Credential-related source omitted]
  • Network accessline 6556
    The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You
  • Network accessline 6567
    {% content-ref url="gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss" %} [tutorial-how-to-fine-tune-gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss)
  • Network accessline 6568
    [tutorial-how-to-fine-tune-gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss)
  • Network accessline 6571
    Currently you cannot load QLoRA fine-tuned gpt-oss models in frameworks other than Unsloth, however you can if you do LoRA fine-tuning and utilize our [bf16 weights](https://huggingface.co/unsloth/gpt-oss-20b-BF16) for fine-tuning. This mea
  • Network accessline 6575
    * gpt-oss-20b [Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) (recommended)
  • Network accessline 6584
    To enable export/usage of the model for use outside of Unsloth but with Hugging Face, llama.cpp, or vLLM, fine-tuning must be done with LoRA while leveraging our [bf16 weights](https://huggingface.co/unsloth/gpt-oss-20b-BF16). Keep in mind
  • Network accessline 6608
    We utilized OpenAI's Triton Kernels library directly to allow MXFP4 inference. For finetuning / training however, the MXFP4 kernels do not yet support training, since the backwards pass is not yet implemented. We're actively working on impl
  • Network accessline 6618
    Though gpt-oss supports only reasoning, you can still fine-tune it with a non-reasoning [dataset](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide), but this may affect its reasoning ability. If you want to maintain
  • Network accessline 6622
    [Credential-related source omitted]
  • Network accessline 6631
    {% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF
  • Network accessline 6632
    [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc.
  • Network accessline 6634
    We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t
  • Network accessline 6637
    > **Quickstart:** Fine-tune gpt-oss-20b for free with our: [Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb)
  • Network accessline 6648
    This section covers fine-tuning gpt-oss using our Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). You can also save and use the gpt-oss notebook into your favorite code editor and follow our [local gpt-oss g
  • Network accessline 6657
    [Credential-related source omitted]
  • Network accessline 6659
    [Credential-related source omitted]
  • Network accessline 6666
    We’ll load **`gpt-oss-20b`**  using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work).&#x20;
  • Network accessline 6675
    [Credential-related source omitted]
  • Network accessline 6679
    [Credential-related source omitted]
  • Network accessline 6686
    Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
  • Network accessline 6688
    {% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.
  • Network accessline 6689
    To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.&#x20;
  • Network accessline 6694
    [Credential-related source omitted]
  • Network accessline 6703
    [Credential-related source omitted]
  • Network accessline 6710
    For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated
  • Network accessline 6716
    [Credential-related source omitted]
  • Network accessline 6745
    [Credential-related source omitted]
  • Network accessline 6747
    One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` ,
  • Network accessline 6749
    {% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 6750
    🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 6753
    Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide).
  • Network accessline 6760
    We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet
  • Network accessline 6764
    [Credential-related source omitted]
  • Network accessline 6768
    [Credential-related source omitted]
  • Network accessline 6779
    [Credential-related source omitted]
  • Network accessline 6783
    [Credential-related source omitted]
  • Network accessline 6812
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 6815
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6816
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 6817
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 6837
    [Credential-related source omitted]
  • Network accessline 6845
    {% hint style="info" %} We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use.
  • Network accessline 6846
    We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use.
  • Network accessline 6854
    Ensure your device is [Unsloth compatible](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and you can read our detailed [installation guide](https://docs.unsloth.ai/get-started/install-and-update).
  • Network accessline 6863
    !uv pip install -qqq \     "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 6864
    "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \     "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
  • Network accessline 6865
    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 6866
    "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
  • Network accessline 6867
    torchvision bitsandbytes \     git+https://github.com/huggingface/transformers \     git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
  • Network accessline 6868
    git+https://github.com/huggingface/transformers \
  • Network accessline 6869
    git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
  • Network accessline 6878
    We’ll load **`gpt-oss-20b`**  using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work for QLoRA fine-tuning). Configur
  • Network accessline 6894
    </strong>    "unsloth/gpt-oss-20b", # 20B model using MXFP4 format     "unsloth/gpt-oss-120b", ] # More models at https://huggingface.co/unsloth
  • Network accessline 6896
    ] # More models at https://huggingface.co/unsloth
  • Network accessline 6915
    Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
  • Network accessline 6917
    {% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.
  • Network accessline 6918
    To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.&#x20;
  • Network accessline 6946
    For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated
  • Network accessline 6990
    [Credential-related source omitted]
  • Network accessline 6992
    One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` ,
  • Network accessline 6994
    {% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 6995
    🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 6998
    Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide).
  • Network accessline 7005
    We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet
  • Network accessline 7035
    [Credential-related source omitted]
  • Network accessline 7064
    [Credential-related source omitted]
  • Network accessline 7091
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 7094
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7095
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7096
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 7124
    As always, feel free to drop by our [Discord](https://discord.com/invite/unsloth) or [Reddit](https://www.reddit.com/r/unsloth/) if you need any help.
  • Network accessline 7130
    Yes you can now [save/export your gpt-oss fine-tuned](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training) model using Unsloth's new update!
  • Network accessline 7142
    Yes! Unsloth now supports RL for gpt-oss with GRPO/GSPO. We made it work on a free Kaggle notebook and achieved the fastest inference for RL. [Read more here](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning)
  • Network accessline 7146
    ***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!*
  • Network accessline 7161
    [Credential-related source omitted]
  • Network accessline 7167
    A huge thank you to Rohan Pandey for his [Flex Attention implementation](https://x.com/khoomeik/status/1955693558914310608), which directly inspired the development of Unsloth's Flex Attention implementation.
  • Network accessline 7173
    OpenAI leveraged **Attention Sinks** from the Efficient Streaming Language Models with Attention Sinks [paper](https://arxiv.org/abs/2309.17453) which shows that you can use a small sliding window, except you must add a global attention on
  • Network accessline 7175
    [Credential-related source omitted]
  • Network accessline 7181
    [Credential-related source omitted]
  • Network accessline 7183
    The paper also shows that the [Attention Is Off By One method](https://www.evanmiller.org/attention-is-off-by-one.html) does partially work, except one must also add a few extra sink tokens to get lower perplexities. **The paper shows that
  • Network accessline 7185
    [Credential-related source omitted]
  • Network accessline 7189
    Flex Attention <https://pytorch.org/blog/flexattention/> is extremely powerful as it provides the practitioner 2 customization routes for the attention mechanism - a **score modifier (f)** and a **masking function (M)**.
  • Network accessline 7207
    By using some visualization utilities from [Flex Attention's Github repo](https://github.com/meta-pytorch/attention-gym), we can visualize this. Assume the sequence length was 16, and a sliding window of 5. On the left is the last sink colu
  • Network accessline 7213
    [Credential-related source omitted]
  • Network accessline 7219
    [Credential-related source omitted]
  • Network accessline 7223
    **Interesting finding**: The official Flex Attention sliding window implementations considers the window size as the number of last tokens **PLUS ONE** as it includes the current token. The HuggingFace and GPT OSS implementations strictly o
  • Network accessline 7240
    [Credential-related source omitted]
  • Network accessline 7246
    [Credential-related source omitted]
  • Network accessline 7250
    We also confirmed through OpenAI's official GPT-OSS implementation on whether we attend to the last N or N+1 tokens here: <https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py>
  • Network accessline 7260
    [Credential-related source omitted]
  • Network accessline 7290
    [Credential-related source omitted]
  • Network accessline 7332
    To run inference on the merged model, you can use vLLM and Llama.cpp among others. OpenAI recommends these [inference settings](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#recommended-settings) for both models: `tempe
  • Network accessline 7336
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 7339
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7340
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7341
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 7368
    # build from source    git clone https://github.com/sgl-project/sglang
  • Network accessline 7369
    git clone https://github.com/sgl-project/sglang
  • Network accessline 7374
    # ROCm 6.3    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3
  • Network accessline 7375
    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3
  • Network accessline 7376
    git clone https://github.com/triton-lang/triton
  • Network accessline 7380
    # hopper    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126
  • Network accessline 7381
    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126
  • Network accessline 7384
    # blackwell cu128    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128
  • Network accessline 7385
    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128
  • Network accessline 7386
    pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl
  • Network accessline 7388
    # blackwell cu129    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129
  • Network accessline 7389
    pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129
  • Network accessline 7390
    pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl
  • Network accessline 7403
    url = f"http://localhost:8000/v1/chat/completions"
  • Network accessline 7410
    response = requests.post(url, json=data)
  • Network accessline 7436
    We [recently collaborated with Hugging Face](https://github.com/huggingface/transformers/pull/40197) to resolve inference issues by using OpenAI’s kernels and ensuring that `swiglu_limit = 7.0` is correctly applied during MXFP4 inference.
  • Network accessline 7442
    [Credential-related source omitted]
  • Network accessline 7454
    [Credential-related source omitted]
  • Network accessline 7458
    OpenAI's sink token implementation is [provided here](https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py). We provide it below:
  • Network accessline 7487
    The HuggingFace transformers implementation is [provided here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py). We also provide it below:
  • Network accessline 7531
    GLM-4.6 is the latest reasoning model from **Z.ai**, achieving SOTA performance on coding and agent benchmarks while offering improved conversational chats. The full 355B parameter model requires **400GB** of disk space, while the Unsloth D
  • Network accessline 7538
    You asked for benchmarks on our quants, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing many full-precision SOTA LLMs. [Read more.](https://docs.unsloth.ai/new/unsloth-dynamic-gg
  • Network accessline 7541
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and Aider performance, meaning you can run & fine-tune quantized GLM LLMs with minimal accuracy loss.
  • Network accessline 7551
    [Credential-related source omitted]
  • Network accessline 7583
    {% stepper %} {% step %} Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp).
  • Network accessline 7585
    Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp).
  • Network accessline 7588
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 7590
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 7590
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 7626
    {% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus
  • Network accessline 7628
    Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 7631
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7632
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7633
    git clone https://github.com/ggerganov/llama.cpp
  • Access to secretsline 7679
    [Credential-related source omitted]
  • Network accessline 7741
    import json openai_client = OpenAI(     base_url = "http://127.0.0.1:8001/v1",
  • Network accessline 7742
    [Credential-related source omitted]
  • Network accessline 7743
    base_url = "http://127.0.0.1:8001/v1",
  • Access to secretsline 7744
    [Credential-related source omitted]
  • Network accessline 7759
    We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple devices resp
  • Network accessline 7761
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/GLM-4.6-GGUF?show_file_info=GLM-4.6-UD-TQ1_
  • Network accessline 7775
    Llama.cpp also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You can also **quantize the KV cache to 4bits** for example to reduce VRAM
  • Network accessline 7802
    <table><thead><tr><th width="249">Dynamic GGUFs</th><th>Dynamic 4-bit + FP8</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/granite-4.0-h-350m-GGUF">H-350M</a></li><li><a href="https:/
  • Network accessline 7804
    You can also view our [Granite-4.0 collection](https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d) for all uploads including Dynamic Float8 quants etc.
  • Network accessline 7841
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 7843
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 7843
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 7854
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 7857
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7858
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 7859
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 7878
    [Credential-related source omitted]
  • Network accessline 7917
    * **Granite-4.0** [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb)
  • Network accessline 7918
    * Granite-4.0-350M [fine-tuning notebook](https://github.com/unslothai/notebooks/blob/main/nb/Granite4.0_350M.ipynb)
  • Network accessline 7924
    [Credential-related source omitted]
  • Network accessline 7953
    DeepSeek’s V3.1 and **Terminus** update introduces hybrid reasoning inference, combining 'think' and 'non-think' into one model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic 2-bit version uses 245GB (-75
  • Network accessline 7955
    {% hint style="success" %} **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\
  • Network accessline 7956
    **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\
  • Network accessline 7958
    [**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing
  • Network accessline 7963
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss.
  • Network accessline 7987
    1. DeepSeek V3.1 is a hybrid reasoning model, meaning you can change the chat template to enable reasoning. The chat template introduced `thinking = True` , but other models use `enable_thinking = True` . We added the option to use `enable_
  • Network accessline 7988
    2. llama.cpp's jinja renderer via [minja](https://github.com/google/minja) does not allow the use of extra arguments in the `.split()` command, so using `.split(text, 1)` works in Python, but not in minja. We had to change this to make llam
  • Network accessline 7995
    According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3.1), these are the recommended settings for V3.1 inference:
  • Network accessline 8060
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 8062
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8062
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8095
    {% step %} Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant.
  • Network accessline 8096
    Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant.
  • Network accessline 8102
    {% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus
  • Network accessline 8104
    Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8107
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8108
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8109
    git clone https://github.com/ggerganov/llama.cpp
  • Access to secretsline 8156
    [Credential-related source omitted]
  • Network accessline 8231
    import json openai_client = OpenAI(     base_url = "http://127.0.0.1:8001/v1",
  • Network accessline 8232
    [Credential-related source omitted]
  • Network accessline 8233
    base_url = "http://127.0.0.1:8001/v1",
  • Access to secretsline 8234
    [Credential-related source omitted]
  • Network accessline 8249
    We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple devices resp
  • Network accessline 8251
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?show_file_info=DeepSeek-
  • Network accessline 8253
    We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-V3.1-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-V3.1).
  • Network accessline 8267
    The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You
  • Network accessline 8286
    Qwen3-Coder is Qwen’s new series of coding agent models, available in 30B (**Qwen3-Coder-Flash**) and 480B parameters. **Qwen3-480B-A35B-Instruct** achieves SOTA coding performance rivalling Claude Sonnet-4, GPT-4.1, and [Kimi K2](https://d
  • Network accessline 8288
    We also uploaded Qwen3-Coder with native <mark style="background-color:purple;">**1M context length**</mark> extended by YaRN and full-precision 8bit and 16bit versions. [Unsloth](https://github.com/unslothai/unsloth) also now supports fine
  • Network accessline 8296
    {% hint style="success" %} **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dy
  • Network accessline 8297
    **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dynamic quant nearly matched
  • Network accessline 8302
    | Dynamic 2.0 GGUF (to run)                                                                                                                                                                                                     | 1M Context Dyn
  • Network accessline 8304
    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF">30B-A3B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF">480B-A35B-Instruct</a></li></ul> | <ul><li><a hre
  • Network accessline 8383
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 8385
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8385
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8396
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8399
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8400
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8401
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 8421
    [Credential-related source omitted]
  • Network accessline 8448
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8451
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8452
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8453
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 8485
    [Credential-related source omitted]
  • Network accessline 8520
    {% hint style="success" %} Also don't forget about the new Qwen3 update. Run [**Qwen3-235B-A22B-Instruct-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) locally with llama.cpp.
  • Network accessline 8521
    Also don't forget about the new Qwen3 update. Run [**Qwen3-235B-A22B-Instruct-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) locally with llama.cpp.
  • Network accessline 8536
    The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You
  • Network accessline 8548
    We also uploaded 1 million context length GGUFs via YaRN scaling [here](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/).
  • Network accessline 8554
    1. Download the first file at <https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/tree/main/UD-Q2\\_K\\_XL> for UD-Q2\_K\_XL, and replace your current file
  • Network accessline 8555
    2. Use `snapshot_download` as usual as in <https://docs.unsloth.ai/basics/qwen3-coder-how-to-run-locally#llama.cpp-run-qwen3-tutorial> which will auto override the old files
  • Network accessline 8556
    3. Use the new chat template via `--chat-template-file`. See [GGUF chat template](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF?chat_template=default) or [chat\_template.jinja](https://huggingface.co/unsloth/Qwen3-Coder
  • Network accessline 8557
    4. As an extra, we also made 1 single 150GB UD-IQ1\_M file (so Ollama works) at <https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF/blob/main/Qwen3-Coder-480B-A35B-Instruct-UD-IQ1\\_M.gguf>
  • Network accessline 8559
    This should solve issues like: <https://github.com/ggml-org/llama.cpp/issues/14915>
  • Network accessline 8635
    Google releases Gemma 3 with a new 270M model and the previous 1B, 4B, 12B, and 27B sizes. The 270M and 1B are text-only, while larger models handle both text and vision. We provide GGUFs, and a guide of how to run it effectively, and how t
  • Network accessline 8637
    {% hint style="success" %} **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co
  • Network accessline 8638
    **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co/collections/unsloth/gemma-
  • Network accessline 8640
    Also see our [Gemma 3n Guide](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune).
  • Network accessline 8647
    * Fine-tune Gemma 3 (4B) with vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb)
  • Network accessline 8656
    | GGUF
  • Network accessline 8658
    | <ul><li><a href="https://huggingface.co/unsloth/gemma-3-270m-it-GGUF">270M</a> - new</li><li><a href="https://huggingface.co/unsloth/gemma-3-1b-it-GGUF">1B</a></li><li><a href="https://huggingface.co/unsloth/gemma-3-4b-it-GGUF">4B</a></li
  • Network accessline 8695
    To run the models on your phone, we recommend using any mobile app that can run GGUFs locally on edge devices like phones. After fine-tuning you can export it to GGUF then run it locally on your phone. Ensure your phone has enough RAM/power
  • Network accessline 8706
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 8708
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8708
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8719
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8722
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8723
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8724
    git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 8738
    3. **OR** download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/gemma-3-27b-i
  • Access to secretsline 8743
    [Credential-related source omitted]
  • Network accessline 8791
    The full input from our <https://unsloth.ai/blog/deepseekr1-dynamic> 1.58bit blog is:
  • Network accessline 8819
    * Try our new [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) which makes the 270M parameter model very smart at playing chess and can predict the next chess move.
  • Network accessline 8820
    * Fine-tune Gemma 3 (4B) using our notebooks for: [**Text**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) or [**Vision**](https://colab.research.google.com/github/unslothai/notebooks/blob/ma
  • Network accessline 8821
    * Or fine-tune [Gemma 3n (E4B)](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune) with [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversa
  • Network accessline 8837
    [Credential-related source omitted]
  • Network accessline 8843
    [Credential-related source omitted]
  • Network accessline 8854
    Google’s Gemma 3n multimodal model handles image, audio, video, and text inputs. Available in 2B and 4B sizes, it supports 140 languages for text and multimodal tasks. You can now run and fine-tune **Gemma-3n-E4B** and **E2B** locally using
  • Network accessline 8856
    > **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 8864
    <table><thead><tr><th width="249">Dynamic 2.0 GGUF (text only)</th><th width="285">Dynamic 4-bit Instruct (to fine-tune)</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/gemma-3n-E2B-it
  • Network accessline 8866
    **See all our Gemma 3n uploads including base and more formats in** [**our collection here**](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339)**.**
  • Dangerous shell operationline 8917
    ollama rm hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
  • Network accessline 8927
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 8929
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8929
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 8940
    {% hint style="info" %} We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp!
  • Network accessline 8941
    We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp!
  • Network accessline 8944
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8947
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8948
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 8949
    git clone https://github.com/ggerganov/llama.cpp
  • Access to secretsline 8967
    [Credential-related source omitted]
  • Network accessline 9018
    Gemma 3n, like [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/..#unsloth-fine-tuning-fixes-for-gemma-3), had issues running on <mark style="background-color:yellow;">**Flotat16 GPUs such as Tesla T4s in Colab**</m
  • Network accessline 9020
    * Fine-tune Gemma 3n-E4B with our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 9021
    * **Audio:** Fine-tune Gemma 3n-E4B with our [**Audio only notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb)
  • Network accessline 9022
    * **Vision**: Fine-tune Gemma 3n-E4B with our [**Vision only notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Vision.ipynb)
  • Network accessline 9044
    * Fine-tune Gemma 3 (4B) with Vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb)
  • Network accessline 9046
    {% hint style="info" %} If you love Kaggle, Google is holding a competition where the best model fine-tuned with Gemma 3n and Unsloth will win a $10K prize! [See more here](https://www.kaggle.com/competitions/google-gemma-3n-hackathon).
  • Network accessline 9047
    If you love Kaggle, Google is holding a competition where the best model fine-tuned with Gemma 3n and Unsloth will win a $10K prize! [See more here](https://www.kaggle.com/competitions/google-gemma-3n-hackathon).
  • Network accessline 9054
    Thanks to discussions from [Michael](https://github.com/mxyng) from the Ollama team and also [Xuan](https://x.com/ngxson) from Hugging Face, there were 2 issues we had to fix specifically for GGUFs:
  • Network accessline 9056
    1. The `add_shared_kv_layers` parameter was accidentally encoded in `float32` which is fine, but becomes slightly complicated to decode on Ollama's side - a simple change to `uint32` solves the issue. [Pull request](https://github.com/ggml-
  • Network accessline 9058
    1. As an [update](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF/discussions/4), [Matt](https://huggingface.co/WBB2500) mentioned we can also use Q4\_0, Q4\_1, Q5\_0, Q5\_1 for the embeddings - and we confirmed it does also work in Oll
  • Network accessline 9066
    Our previous fixes for Gemma 3 is [discussed here](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune). For Gemma 3, we found that activations exceed float16's maximum range of **65504.**
  • Network accessline 9073
    [Credential-related source omitted]
  • Network accessline 9079
    [Credential-related source omitted]
  • Network accessline 9103
    We found Gemma 3N's vision encoder to be quite unique as well since it re-uses hidden states. This unfortunately limits the usage of [Unsloth's gradient checkpointing](https://unsloth.ai/blog/long-context), which could have reduced VRAM usa
  • Network accessline 9114
    [Credential-related source omitted]
  • Network accessline 9116
    {% hint style="success" %} **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 9117
    **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 9124
    So what is so special about Gemma 3n you ask? It is based on [Matryoshka Transformer or MatFormer](https://arxiv.org/abs/2310.07707) architecture meaning that each transformer layer/block embeds/nests FFNs of progressively smaller sizes. Th
  • Network accessline 9132
    [Credential-related source omitted]
  • Network accessline 9134
    {% hint style="info" %} **Fine-tune and try multimodal Gemma 3n inference with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 9135
    **Fine-tune and try multimodal Gemma 3n inference with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 9145
    {% hint style="success" %} **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507)
  • Network accessline 9146
    **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507)
  • Network accessline 9149
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Qwen LLMs with minimal accuracy loss.
  • Network accessline 9153
    [Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3 and Qwen3 MOE models — 2x faster, with 70% less VR
  • Network accessline 9159
    | Dynamic 2.0 GGUF (to run)
  • Network accessline 9161
    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-0.6B-GGUF">0.6B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-1.7B-GGUF">1.7B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-4B-GGUF">4B</a></li><li><a href="htt
  • Network accessline 9208
    Qwen3 models come with built-in "thinking mode" to boost reasoning and improve response quality - similar to how [QwQ-32B](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/qwq-32b-how-to-run-effectively) worked. Instru
  • Network accessline 9275
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 9277
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 9298
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 9301
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9303
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 9315
    [Credential-related source omitted]
  • Access to secretsline 9359
    [Credential-related source omitted]
  • Network accessline 9405
    * [Qwen3 (14B) Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) (recommended)
  • Network accessline 9406
    * [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **- Advanced GRPO LoRA**
  • Network accessline 9407
    * [Qwen3 (14B) Alpaca notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Alpaca.ipynb) (for Base models)
  • Network accessline 9440
    [Credential-related source omitted]
  • Network accessline 9450
    If you'd like to read a full end-to-end guide on how to use Unsloth notebooks for fine-tuning or just learn about fine-tuning, creating [datasets](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) etc., view our [co
  • Network accessline 9452
    {% content-ref url="../get-started/fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
  • Network accessline 9453
    [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
  • Network accessline 9456
    {% content-ref url="../get-started/fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
  • Network accessline 9457
    [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
  • Network accessline 9465
    [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **notebook - Advanced GRPO LoRA**
  • Network accessline 9475
    [Credential-related source omitted]
  • Network accessline 9482
    Qwen released 2507 (July 2025) updates for their [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune) 4B, 30B and 235B models, introducing both "thinking" and "non-thinking" variants. The non-thinking '**Qwen3-30B-A3B-Inst
  • Network accessline 9486
    [Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3-2507 models — 2x faster, with 70% less VRAM, and 8
  • Network accessline 9490
    **Unsloth** [**Dynamic 2.0**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **GGUFs:**
  • Network accessline 9492
    | Model                    | GGUFs to run:                                                                                                                                                 | | ------------------------ | ----------------------
  • Network accessline 9493
    | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Qwen3-**4B-2507**        | [Instruct](https://hug
  • Network accessline 9494
    | Qwen3-**4B-2507**        | [Instruct](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF) • [Thinking ](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF)              |
  • Network accessline 9495
    | Qwen3-**30B-A3B**-2507   | [Instruct](#llama.cpp-run-qwen3-30b-a3b-instruct-2507-tutorial) • [Thinking](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF)                 |
  • Network accessline 9496
    | Qwen3-**235B-A22B**-2507 | [Instruct](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF) • [Thinking](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF) |
  • Network accessline 9555
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 9568
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 9571
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9573
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 9593
    [Credential-related source omitted]
  • Network accessline 9623
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 9636
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 9639
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9640
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9641
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 9661
    [Credential-related source omitted]
  • Network accessline 9697
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 9700
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9701
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9702
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 9730
    [Credential-related source omitted]
  • Network accessline 9791
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 9794
    apt-get update    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9795
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 9796
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 9824
    [Credential-related source omitted]
  • Network accessline 9870
    The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You
  • Network accessline 9884
    Unsloth makes [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/..#fine-tuning-qwen3-with-unsloth) and Qwen3-2507 fine-tuning 2x faster, use 70% less VRAM and supports 8x longer context lengths.  Because Qwen3-2507 was o
  • Network accessline 9888
    * [Qwen3 (14B) Reasoning + Conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)&#x20;
  • Network accessline 9900
    **Qwen3-2507-4B notebooks for:** [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) and [Instruct](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen
  • Network accessline 9921
    [Credential-related source omitted]
  • Network accessline 9928
    [Credential-related source omitted]
  • Network accessline 9935
    DeepSeek-R1-0528 is DeepSeek's new update to their R1 reasoning model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic **1.66-bit** version uses 162GB (-80% reduction in size). GGUF: [DeepSeek-R1-0528-GGUF]
  • Network accessline 9937
    DeepSeek also released a R1-0528 distilled version by fine-tuning Qwen3 (8B). The distill achieves similar performance to Qwen3 (235B). ***You can also*** [***fine-tune Qwen3 Distill***](#fine-tuning-deepseek-r1-0528-with-unsloth) ***with U
  • Network accessline 9939
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss.
  • Network accessline 9946
    NEW: Huge improvements to tool calling and chat template fixes.\ \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM
  • Network accessline 9947
    \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/
  • Network accessline 9948
    New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/De
  • Network accessline 9967
    According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528), these are the recommended settings for R1 (R1-0528 and Qwen3 distill should use the same settings) inference:
  • Network accessline 9994
    * Qwen3 (8B) distill: [DeepSeek-R1-0528-Qwen3-8B-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)
  • Network accessline 9997
    We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple device
  • Network accessline 9999
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSe
  • Network accessline 10001
    We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-R1-0528).
  • Network accessline 10010
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 10031
    Open WebUI has made an step-by-step tutorial on how to run R1 here and for R1-0528, you will just need to replace R1 with the new 0528 quant: [docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/](https://docs.openwebui.com/tutoria
  • Network accessline 10051
    1. <mark style="background-color:yellow;">**To run the full 720GB R1-0528 model,**</mark> [<mark style="background-color:yellow;">**see here**</mark>](#run-full-r1-0528-on-llama.cpp)<mark style="background-color:yellow;">**.**</mark> Obtain
  • Network accessline 10054
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 10055
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 10056
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 10071
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 10074
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 10075
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 10076
    git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 10113
    3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . We <mark style="background-color:green;">**recommend using
  • Access to secretsline 10120
    [Credential-related source omitted]
  • Network accessline 10157
    You can also test our dynamic quants via [r/Localllama](https://www.reddit.com/r/LocalLLaMA/comments/1j7r47l/i_just_made_an_animation_of_a_ball_bouncing/) which tests the model on creating a basic physics engine to simulate balls rotating i
  • Network accessline 10159
    [Credential-related source omitted]
  • Network accessline 10192
    * [**DeepSeek-R1-0528-Qwen3-8B notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) **- new**
  • Network accessline 10209
    **Magistral-Small-2509** is a reasoning LLM developed by Mistral AI. It excels at coding and mathematics and supports multiple languages.  Magistral supports a 128k token context window and was finetuned from [**Mistral-Small-3.2**](https:/
  • Network accessline 10221
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Mistral LLMs with minimal accuracy loss.
  • Network accessline 10225
    <table><thead><tr><th width="255.64999389648438">Dynamic 2.0 GGUF (to run)</th><th width="305.25">Dynamic 4-bit (to finetune/deploy)</th><th>Dynamic Float8</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/Magi
  • Network accessline 10310
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 10315
    2. Run the model with our dynamic quant. We did not set the context length automatically, so it will just use Ollama's default set context length.\    Note you can call `ollama serve &`in another terminal if it fails! We include all suggest
  • Network accessline 10316
    Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Magistral supports 40K context lengths, so best to enable [**KV cache q
  • Network accessline 10317
    3. Also Magistral supports 40K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves
  • Network accessline 10318
    4. **Ollama also sets the default context length to 4096**, as [mentioned here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size). Use `OLLAMA_CONTEXT_LENGTH=8192` to change it to 8192. Magist
  • Network accessline 10328
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 10331
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 10333
    git clone https://github.com/ggml-org/llama.cpp
  • Access to secretsline 10359
    [Credential-related source omitted]
  • Network accessline 11514
    For Magistral versions before September 2025, [Xuan-Son](https://x.com/ngxson) from HuggingFace showed in their [GGUF repo](https://huggingface.co/ngxson/Devstral-Small-Vision-2505-GGUF) how it is actually possible to "graft" the vision enc
  • Network accessline 11537
    * **Magistral 2509 Kaggle (2x Tesla T4s) free** [**finetuning notebook**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=
  • Network accessline 11538
    * Magistral 2509 Colab L4 (24GB) [finetuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Magistral_\(24B\)-Reasoning-Conversational.ipynb)
  • Network accessline 11540
    Magistral slightly exceeds the memory limits of a 16GB VRAM, so fine-tuning it for free on Google Colab isn't possible for now. However, you *can* fine-tune the model for free using [Kaggle](https://www.kaggle.com/danielhanchen/code), which
  • Network accessline 11542
    **To finetune on new reasoning traces, you can use our free** [**Kaggle notebook for Magistral**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversati
  • Network accessline 11569
    * [vLLM's Float8 format](https://huggingface.co/unsloth/Magistral-Small-2509-FP8-Dynamic)
  • Network accessline 11570
    * [TorchAO's Float8 format](https://huggingface.co/unsloth/Magistral-Small-2509-FP8-torchao)
  • Network accessline 11572
    Both are fantastic to deploy via vLLM. Read up on using TorchAO based FP8 quants in vLLM [here](https://docs.vllm.ai/en/latest/features/quantization/torchao.html).
  • Network accessline 11601
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td>33.8GB</td><td><a href="https://huggingface.co/unsloth/Llama-4-
  • Network accessline 11609
    | MoE Bits | Type      | Disk Size | HF Link                                                                                             | | -------- | --------- | --------- | ----------------------------------------------------------------
  • Network accessline 11610
    | -------- | --------- | --------- | --------------------------------------------------------------------------------------------------- | | 1.78bit  | IQ1\_S    | 122GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-
  • Network accessline 11611
    | 1.78bit  | IQ1\_S    | 122GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_S)   |
  • Network accessline 11612
    | 1.93bit  | IQ1\_M    | 128GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_M)   |
  • Network accessline 11613
    | 2.42-bit | IQ2\_XXS  | 140GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ2_XXS) |
  • Network accessline 11614
    | 2.71-bit | Q2\_K\_XL | 151B      | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q2_K_XL) |
  • Network accessline 11615
    | 3.5-bit  | Q3\_K\_XL | 193GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q3_K_XL) |
  • Network accessline 11616
    | 4.5-bit  | Q4\_K\_XL | 243GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q4_K_XL) |
  • Network accessline 11635
    * A BOS token of `<|begin_of_text|>` is auto added during tokenization (do NOT add it manually!)&#x20; * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow
  • Network accessline 11636
    * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow;">**suggested optional system prompt**</mark>, which is listed below:
  • Network accessline 11654
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 11657
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11658
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11659
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 11666
    2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/Llama-4-Scout-17B-16
  • Access to secretsline 11671
    [Credential-related source omitted]
  • Access to secretsline 11718
    [Credential-related source omitted]
  • Network accessline 11753
    [Credential-related source omitted]
  • Network accessline 11755
    For Llama 4 Scout, we found we should not quantize the vision layers, and leave the MoE router and some other layers as unquantized - we upload these to <https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-dynamic-bnb-4bit
  • Network accessline 11757
    [Credential-related source omitted]
  • Network accessline 11759
    We also had to convert `torch.nn.Parameter` to `torch.nn.Linear` for the MoE layers to allow 4bit quantization to occur. This also means we had to rewrite and patch over the generic Hugging Face implementation. We upload our quantized versi
  • Network accessline 11761
    [Credential-related source omitted]
  • Network accessline 11770
    Kimi-K2-Instruct-0905 the new version of K2 achieves SOTA performance in knowledge, reasoning, coding, and agentic tasks. The full 1T parameter model from Moonshot AI requires 1.09TB of disk space, while the quantized **Unsloth Dynamic 1.8-
  • Network accessline 11772
    You can now run **Kimi-K2-Instruct-0905** with our new GGUFs. Use our same settings below but ensure you change the model name from 'Kimi-K2-Instruct' to 'Kimi-K2-Instruct-0905': [K2-0905 GGUFs](https://huggingface.co/unsloth/Kimi-K2-Instru
  • Network accessline 11774
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized LLMs with minimal accuracy loss.
  • Network accessline 11776
    <a href="https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally#run-kimi-k2-tutorials" class="button primary">Run in llama.cpp</a>
  • Network accessline 11800
    According to [Moonshot AI](https://huggingface.co/moonshotai/Kimi-K2-Instruct), these are the recommended settings for Kimi K2 inference:
  • Network accessline 11846
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/Kimi-K2-Instruct-GGUF/tree/main/UD-TQ1_0">U
  • Network accessline 11848
    We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/Kimi-K2-Instruct-BF16).
  • Network accessline 11852
    {% hint style="success" %} You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model:
  • Network accessline 11853
    You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model:
  • Network accessline 11858
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 11861
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11862
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11863
    git clone https://github.com/ggml-org/llama.cpp
  • Network accessline 11898
    3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-TQ1_0`(dynamic 1.8bit quant) or other quantized versions like `Q2_K_XL` . We <mark style="background-color:green;">**recommend using
  • Access to secretsline 11905
    [Credential-related source omitted]
  • Network accessline 11916
    {% hint style="info" %} If you find that downloads get stuck at 90 to 95% or so, please see <https://docs.unsloth.ai/basics/troubleshooting-and-faqs#downloading-gets-stuck-at-90-to-95>
  • Network accessline 11917
    If you find that downloads get stuck at 90 to 95% or so, please see <https://docs.unsloth.ai/basics/troubleshooting-and-faqs#downloading-gets-stuck-at-90-to-95>
  • Network accessline 11944
    **16th July 2025: Kimi K2 updated their tokenizer to enable multiple tool calls** as per <https://x.com/Kimi_Moonshot/status/1945050874067476962>
  • Network accessline 11946
    **18th July 2025: We fixed a system prompt - Kimi tweeted about our fix as well here:** [**https://x.com/Kimi\_Moonshot/status/1946130043446690030**](https://x.com/Kimi_Moonshot/status/1946130043446690030)**. The fix was described here as w
  • Network accessline 11951
    wget https://huggingface.co/unsloth/Kimi-K2-Instruct/raw/main/chat_template.jinja
  • Network accessline 11955
    The Kimi K2 tokenizer was interesting to play around with - <mark style="background-color:green;">**it's mostly similar in action to GPT-4o's tokenizer**</mark>! We first see in the [tokenization\_kimi.py](https://huggingface.co/moonshotai/
  • Network accessline 11972
    After careful inspection, we find Kimi K2 is nearly identical to GPT-4o's tokenizer regex which can be found in [llama.cpp's source code](https://github.com/ggml-org/llama.cpp/blob/55c509daf51d25bfaee9c8b8ce6abff103d4473b/src/llama-vocab.cp
  • Network accessline 11982
    Both tokenize numbers into groups of 1 to 3 numbers (9, 99, 999), and use similar patterns. The only difference looks to be the handling of "Han" or Chinese characters, which Kimi's tokenizer deals with more. [The PR](https://github.com/ggm
  • Network accessline 11988
    We introduced the Flappy Bird test when our 1.58bit quants for DeepSeek R1 were provided. We found Kimi K2 one of the only models to one-shot all our tasks including this one, [Heptagon ](https://docs.unsloth.ai/models/deepseek-r1-0528-how-
  • Network accessline 12007
    You can also test the dynamic quants via the Heptagon Test as per [r/Localllama](https://www.reddit.com/r/LocalLLaMA/comments/1j7r47l/i_just_made_an_animation_of_a_ball_bouncing/) which tests the model on creating a basic physics engine to
  • Network accessline 12009
    <figure><img src="https://docs.unsloth.ai/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252F2O72oTw5yPUbcxXjDNKS%252Fsnapsh
  • Network accessline 12026
    You can now run **Grok 2** (aka Grok 2.5), the 270B parameter model by xAI. Full precision requires **539GB**, while the Unsloth Dynamic 3-bit version shrinks size down to just **118GB** (a 75% reduction). GGUF: [Grok-2-GGUF](https://huggin
  • Network accessline 12028
    The **3-bit Q3\_K\_XL** model runs on a single **128GB Mac** or **24GB VRAM + 128GB RAM**, achieving **5+ tokens/s** inference. Thanks to the llama.cpp team and community for [supporting Grok 2](https://github.com/ggml-org/llama.cpp/pull/15
  • Network accessline 12030
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized Grok LLMs with minimal accuracy loss.
  • Network accessline 12064
    {% stepper %} {% step %} Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if
  • Network accessline 12066
    Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or
  • Network accessline 12069
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 12070
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 12071
    git clone https://github.com/ggml-org/llama.cpp cd llama.cpp && git fetch origin pull/15539/head:MASTER && git checkout MASTER && cd .. cmake llama.cpp -B llama.cpp/build \
  • Access to secretsline 12118
    [Credential-related source omitted]
  • Network accessline 12156
    | MoE Bits | Type + Link                                                                         | Disk Size   | Details       | | -------- | ----------------------------------------------------------------------------------- | -----------
  • Network accessline 12157
    | -------- | ----------------------------------------------------------------------------------- | ----------- | ------------- | | 1.66bit  | [TQ1\_0](https://huggingface.co/unsloth/grok-2-GGUF/blob/main/grok-2-UD-TQ1_0.gguf) | **81.8 GB**
  • Network accessline 12158
    | 1.66bit  | [TQ1\_0](https://huggingface.co/unsloth/grok-2-GGUF/blob/main/grok-2-UD-TQ1_0.gguf) | **81.8 GB** | 1.92/1.56bit  |
  • Network accessline 12159
    | 1.78bit  | [IQ1\_S](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ1_S)             | **88.9 GB** | 2.06/1.56bit  |
  • Network accessline 12160
    | 1.93bit  | [IQ1\_M](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ1_M)             | **94.5 GB** | 2.5/2.06/1.56 |
  • Network accessline 12161
    | 2.42bit  | [IQ2\_XXS](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ2_XXS)         | **99.3 GB** | 2.5/2.06bit   |
  • Network accessline 12162
    | 2.71bit  | [Q2\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q2_K_XL)        | **112 GB**  | 3.5/2.5bit    |
  • Network accessline 12163
    | 3.12bit  | [IQ3\_XXS](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-IQ3_XXS)         | **117 GB**  | 3.5/2.06bit   |
  • Network accessline 12164
    | 3.5bit   | [Q3\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q3_K_XL)        | **126 GB**  | 4.5/3.5bit    |
  • Network accessline 12165
    | 4.5bit   | [Q4\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q4_K_XL)        | **155 GB**  | 5.5/4.5bit    |
  • Network accessline 12166
    | 5.5bit   | [Q5\_K\_XL](https://huggingface.co/unsloth/grok-2-GGUF/tree/main/UD-Q5_K_XL)        | **191 GB**  | 6.5/5.5bit    |
  • Network accessline 12180
    The [latest llama.cpp release](https://github.com/ggml-org/llama.cpp/pull/14363) also introduces high throughput mode. Use `llama-parallel`. Read more about it [here](https://github.com/ggml-org/llama.cpp/tree/master/examples/parallel). You
  • Network accessline 12201
    Finetuned from [**Mistral-Small-3.1**](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF), Devstral supports a 128k context window. Devstral Small 1.1 has improved performance, achieving a score of 53.6% performance on

skills/mlops/training/unsloth/references/llms-txt.md

  • Access to secretsline 12
    [Credential-related source omitted]
  • Network accessline 35
    {% hint style="success" %} Read more on running Llama 4 here: <https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4>
  • Network accessline 36
    Read more on running Llama 4 here: <https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4>
  • Network accessline 75
    Qwen3-Coder is Qwen’s new series of coding agent models, available in 30B (**Qwen3-Coder-Flash**) and 480B parameters. **Qwen3-480B-A35B-Instruct** achieves SOTA coding performance rivalling Claude Sonnet-4, GPT-4.1, and [Kimi K2](https://d
  • Network accessline 77
    We also uploaded Qwen3-Coder with native <mark style="background-color:purple;">**1M context length**</mark> extended by YaRN and full-precision 8bit and 16bit versions. [Unsloth](https://github.com/unslothai/unsloth) also now supports fine
  • Network accessline 85
    {% hint style="success" %} **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dy
  • Network accessline 86
    **Does** [**Unsloth Dynamic Quants**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **work?** Yes, and very well. In third-party testing on the Aider Polyglot benchmark, the **UD-Q4\_K\_XL (276GB)** dynamic quant nearly matched
  • Network accessline 91
    | Dynamic 2.0 GGUF (to run)                                                                                                                                                                                                     | 1M Context Dyn
  • Network accessline 93
    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF">30B-A3B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF">480B-A35B-Instruct</a></li></ul> | <ul><li><a hre
  • Network accessline 147
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 186
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 188
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 188
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 236
    Orpheus supports tags like `<laugh>`, `<chuckle>`, `<sigh>`, `<cough>`, `<sniffle>`, `<groan>`, `<yawn>`, `<gasp>`, etc. For example: `"I missed you <laugh> so much!"`.  These tags are enclosed in angle brackets and will be treated as speci
  • Network accessline 299
    Unsloth model catalog for all our [Dynamic](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) GGUF, 4-bit, 16-bit models on Hugging Face.
  • Network accessline 309
    | Model                                                                                      | Variant                | GGUF                                                                            | Instruct (4-bit)
  • Network accessline 310
    | ------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------- | --------------------------------------
  • Network accessline 311
    | [**gpt-oss** ](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune)            | 120b                   | [link](https://huggingface.co/unsloth/gpt-oss-120b-GGUF)                        | [link](https://huggingface.co/unsloth/
  • Network accessline 312
    |                                                                                            | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b-GGUF)                         | [link](https://huggingface.co/unsloth/
  • Network accessline 313
    | [**DeepSeek-V3.1**](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally)       | Terminus               | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)              | —
  • Network accessline 314
    |                                                                                            | V3.1                   | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF)                       | —
  • Network accessline 315
    | [**Qwen3-VL**](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune)           | 2B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 316
    |                                                                                            | 2B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 317
    |                                                                                            | 4B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 318
    |                                                                                            | 4B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-4B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 319
    |                                                                                            | 8B-Instruct            | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 320
    |                                                                                            | 8B-Thinking            | [link](https://huggingface.co/unsloth/Qwen3-VL-8B-Thinking-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 321
    |                                                                                            | 30B-A3B-Instruct       | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Instruct-GGUF)           | —
  • Network accessline 322
    |                                                                                            | 30B-A3B-Thinking       | [link](https://huggingface.co/unsloth/Qwen3-VL-30B-A3B-Thinking-GGUF)           | —
  • Network accessline 323
    |                                                                                            | 32B-Instruct           | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Instruct-GGUF)               | [link](https://huggingface.co/unsloth/
  • Network accessline 324
    |                                                                                            | 32B-Thinking           | [link](https://huggingface.co/unsloth/Qwen3-VL-32B-Thinking-GGUF)               | [link](https://huggingface.co/unsloth/
  • Network accessline 325
    |                                                                                            | 235B-A22B-Instruct     | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Instruct-GGUF)         | —
  • Network accessline 326
    |                                                                                            | 235B-A22B-Thinking     | [link](https://huggingface.co/unsloth/Qwen3-VL-235B-A22B-Thinking-GGUF)         | —
  • Network accessline 327
    | [**Qwen3-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) | 30B-A3B-Instruct       | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF)         | —
  • Network accessline 328
    |                                                                                            | 30B-A3B-Thinking       | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF)         | —
  • Network accessline 329
    |                                                                                            | 235B-A22B-Thinking     | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF/)      | —
  • Network accessline 330
    |                                                                                            | 235B-A22B-Instruct     | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF/)      | —
  • Network accessline 331
    | **Qwen3-Coder**                                                                            | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF)        | —
  • Network accessline 332
    |                                                                                            | 480B-A35B              | [link](https://huggingface.co/unsloth/Qwen3-Coder-480B-A35B-Instruct-GGUF)      | —
  • Network accessline 333
    | **Granite-4.0 (new)**                                                                      | H-Small                | [link](https://huggingface.co/unsloth/granite-4.0-h-small-GGUF)                 | [link](https://huggingface.co/unsloth/
  • Network accessline 334
    | **GLM (new)**                                                                              | 4.6                    | [link](https://huggingface.co/unsloth/GLM-4.6-GGUF)                             | —
  • Network accessline 335
    |                                                                                            | 4.5-Air                | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF)                         | —
  • Network accessline 336
    | **Kimi-K2-0905**                                                                           | 1T                     | [link](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF)               | —
  • Network accessline 337
    | **Gemma 3n**                                                                               | E2B                    | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 338
    |                                                                                            | E4B                    | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 339
    | **DeepSeek-R1-0528**                                                                       | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)           | [link](https://huggingface.co/unsloth/
  • Network accessline 340
    |                                                                                            | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF)                    | —
  • Network accessline 341
    | **Mistral**                                                                                | Magistral Small (2509) | [link](https://huggingface.co/unsloth/Magistral-Small-2509-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 342
    |                                                                                            | Magistral Small (2507) | [link](https://huggingface.co/unsloth/Magistral-Small-2507-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 343
    |                                                                                            | Small 3.2 24B (2506)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF) | [link](https://huggingface.co/unsloth/
  • Network accessline 344
    | FLUX.1                                                                                     | Kontext-dev            | [link](https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF)                  | —
  • Network accessline 345
    | **Qwen3**                                                                                  | 0.6 B                  | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF)                          | [link](https://huggingface.co/unsloth/
  • Network accessline 346
    |                                                                                            | 1.7 B                  | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF)                          | [link](https://huggingface.co/unsloth/
  • Network accessline 347
    |                                                                                            | 4 B                    | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF)                            | [link](https://huggingface.co/unsloth/
  • Network accessline 348
    |                                                                                            | 8 B                    | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF)                            | [link](https://huggingface.co/unsloth/
  • Network accessline 349
    |                                                                                            | 14 B                   | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF)                           | [link](https://huggingface.co/unsloth/
  • Network accessline 350
    |                                                                                            | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF)                       | [link](https://huggingface.co/unsloth/
  • Network accessline 351
    |                                                                                            | 32 B                   | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF)                           | [link](https://huggingface.co/unsloth/
  • Network accessline 352
    |                                                                                            | 235B-A22B              | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF)                     | —
  • Network accessline 353
    | **Llama 4**                                                                                | Scout 17B 16E          | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF)      | [link](https://huggingface.co/unsloth/
  • Network accessline 354
    |                                                                                            | Maverick 17B 128E      | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF)  | —
  • Network accessline 355
    | **Grok 2**                                                                                 | 270B                   | [link](https://huggingface.co/unsloth/grok-2-GGUF)                              | —
  • Network accessline 356
    | **Qwen-2.5 Omni**                                                                          | 3 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF)                     | —
  • Network accessline 357
    |                                                                                            | 7 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF)                     | —
  • Network accessline 358
    | **Phi-4**                                                                                  | Reasoning-plus         | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF)                | [link](https://huggingface.co/unsloth/
  • Network accessline 359
    |                                                                                            | Reasoning              | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF)                     | [link](https://huggingface.co/unsloth/
  • Network accessline 361
    | Model             | Variant                | GGUF                                                                      | Instruct (4-bit)                                                                      | | ----------------- | -------
  • Network accessline 362
    | ----------------- | ---------------------- | ------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | **DeepSeek-V3.1** | Terminu
  • Network accessline 363
    | **DeepSeek-V3.1** | Terminus               | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)        |                                                                                       |
  • Network accessline 364
    |                   | V3.1                   | [link](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF)                 |                                                                                       |
  • Network accessline 365
    | **DeepSeek-V3**   | V3-0324                | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF)              | —                                                                                     |
  • Network accessline 366
    |                   | V3                     | [link](https://huggingface.co/unsloth/DeepSeek-V3-GGUF)                   | —                                                                                     |
  • Network accessline 367
    | **DeepSeek-R1**   | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF)              | —                                                                                     |
  • Network accessline 368
    |                   | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)     | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit)     |
  • Network accessline 369
    |                   | R1                     | [link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF)                   | —                                                                                     |
  • Network accessline 370
    |                   | R1 Zero                | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero-GGUF)              | —                                                                                     |
  • Network accessline 371
    |                   | Distill Llama 3 8 B    | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit)  |
  • Network accessline 372
    |                   | Distill Llama 3.3 70 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-bnb-4bit)         |
  • Network accessline 373
    |                   | Distill Qwen 2.5 1.5 B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-GGUF) | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit) |
  • Network accessline 374
    |                   | Distill Qwen 2.5 7 B   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF)   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit)   |
  • Network accessline 375
    |                   | Distill Qwen 2.5 14 B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit)  |
  • Network accessline 376
    |                   | Distill Qwen 2.5 32 B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF)  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit)          |
  • Network accessline 378
    | Model         | Variant             | GGUF                                                                           | Instruct (4-bit)                                                                       | | ------------- | ------------
  • Network accessline 379
    | ------------- | ------------------- | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- | | **Llama 4**   | Scout 17 B-1
  • Network accessline 380
    | **Llama 4**   | Scout 17 B-16 E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF)     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) |
  • Network accessline 381
    |               | Maverick 17 B-128 E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF) | —                                                                                      |
  • Network accessline 382
    | **Llama 3.3** | 70 B                | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF)             | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-bnb-4bit)                 |
  • Network accessline 383
    | **Llama 3.2** | 1 B                 | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)                  |
  • Network accessline 384
    |               | 3 B                 | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit)                  |
  • Network accessline 385
    |               | 11 B Vision         | —                                                                              | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 386
    |               | 90 B Vision         | —                                                                              | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit)          |
  • Network accessline 387
    | **Llama 3.1** | 8 B                 | [link](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit)             |
  • Network accessline 388
    |               | 70 B                | —                                                                              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct-bnb-4bit)            |
  • Network accessline 389
    |               | 405 B               | —                                                                              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct-bnb-4bit)           |
  • Network accessline 390
    | **Llama 3**   | 8 B                 | —                                                                              | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit)                    |
  • Network accessline 391
    |               | 70 B                | —                                                                              | [link](https://huggingface.co/unsloth/llama-3-70b-bnb-4bit)                            |
  • Network accessline 392
    | **Llama 2**   | 7 B                 | —                                                                              | [link](https://huggingface.co/unsloth/llama-2-7b-chat-bnb-4bit)                        |
  • Network accessline 393
    |               | 13 B                | —                                                                              | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit)                            |
  • Network accessline 394
    | **CodeLlama** | 7 B                 | —                                                                              | [link](https://huggingface.co/unsloth/codellama-7b-bnb-4bit)                           |
  • Network accessline 395
    |               | 13 B                | —                                                                              | [link](https://huggingface.co/unsloth/codellama-13b-bnb-4bit)                          |
  • Network accessline 396
    |               | 34 B                | —                                                                              | [link](https://huggingface.co/unsloth/codellama-34b-bnb-4bit)                          |
  • Network accessline 398
    | Model        | Variant       | GGUF                                                         | Instruct (4-bit)                                                             | | ------------ | ------------- | --------------------------------
  • Network accessline 399
    | ------------ | ------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------- | | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/u
  • Network accessline 400
    | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit)      |
  • Possible obfuscationline 400
    | **Gemma 3n** | E2B           | <U+200B>[link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit)      |
  • Network accessline 401
    |              | E4B           | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF)  | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit)      |
  • Network accessline 402
    | **Gemma 3**  | 270M          | [link](https://huggingface.co/unsloth/gemma-3-270m-it-GGUF)  | [link](https://huggingface.co/unsloth/gemma-3-270m-it)                       |
  • Network accessline 403
    |              | 1 B           | [link](https://huggingface.co/unsloth/gemma-3-1b-it-GGUF)    | [link](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit)        |
  • Network accessline 404
    |              | 4 B           | [link](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF)    | [link](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit)        |
  • Network accessline 405
    |              | 12 B          | [link](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF)   | [link](https://huggingface.co/unsloth/gemma-3-12b-it-unsloth-bnb-4bit)       |
  • Network accessline 406
    |              | 27 B          | [link](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF)   | [link](https://huggingface.co/unsloth/gemma-3-27b-it-unsloth-bnb-4bit)       |
  • Network accessline 407
    | **MedGemma** | 4 B (vision)  | [link](https://huggingface.co/unsloth/medgemma-4b-it-GGUF)   | [link](https://huggingface.co/unsloth/medgemma-4b-it-unsloth-bnb-4bit)       |
  • Network accessline 408
    |              | 27 B (vision) | [link](https://huggingface.co/unsloth/medgemma-27b-it-GGUF)  | [link](https://huggingface.co/unsloth/medgemma-27b-text-it-unsloth-bnb-4bit) |
  • Network accessline 409
    | **Gemma 2**  | 2 B           | [link](https://huggingface.co/unsloth/gemma-2-it-GGUF)       | [link](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit)                |
  • Network accessline 410
    |              | 9 B           | —                                                            | [link](https://huggingface.co/unsloth/gemma-2-9b-it-bnb-4bit)                |
  • Network accessline 411
    |              | 27 B          | —                                                            | [link](https://huggingface.co/unsloth/gemma-2-27b-it-bnb-4bit)               |
  • Network accessline 413
    | Model                      | Variant    | GGUF                                                                         | Instruct (4-bit)                                                                | | -------------------------- | ----
  • Network accessline 414
    | -------------------------- | ---------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | | **Qwen 3**                 | 0.6
  • Network accessline 415
    | **Qwen 3**                 | 0.6 B      | [link](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF)                       | [link](https://huggingface.co/unsloth/Qwen3-0.6B-unsloth-bnb-4bit)              |
  • Network accessline 416
    |                            | 1.7 B      | [link](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF)                       | [link](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit)              |
  • Network accessline 417
    |                            | 4 B        | [link](https://huggingface.co/unsloth/Qwen3-4B-GGUF)                         | [link](https://huggingface.co/unsloth/Qwen3-4B-unsloth-bnb-4bit)                |
  • Network accessline 418
    |                            | 8 B        | [link](https://huggingface.co/unsloth/Qwen3-8B-GGUF)                         | [link](https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit)                |
  • Network accessline 419
    |                            | 14 B       | [link](https://huggingface.co/unsloth/Qwen3-14B-GGUF)                        | [link](https://huggingface.co/unsloth/Qwen3-14B-unsloth-bnb-4bit)               |
  • Network accessline 420
    |                            | 30 B-A3B   | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF)                    | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-bnb-4bit)                   |
  • Network accessline 421
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen3-32B-GGUF)                        | [link](https://huggingface.co/unsloth/Qwen3-32B-unsloth-bnb-4bit)               |
  • Network accessline 422
    |                            | 235 B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF)                  | —                                                                               |
  • Network accessline 423
    | **Qwen 2.5 Omni**          | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF)                  | —                                                                               |
  • Network accessline 424
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF)                  | —                                                                               |
  • Network accessline 425
    | **Qwen 2.5 VL**            | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF)           | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 426
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF)           | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit)  |
  • Network accessline 427
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-GGUF)          | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit) |
  • Network accessline 428
    |                            | 72 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-GGUF)          | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct-unsloth-bnb-4bit) |
  • Network accessline 429
    | **Qwen 2.5**               | 0.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit)           |
  • Network accessline 430
    |                            | 1.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit)           |
  • Network accessline 431
    |                            | 3 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct-bnb-4bit)             |
  • Network accessline 432
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-bnb-4bit)             |
  • Network accessline 433
    |                            | 14 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct-bnb-4bit)            |
  • Network accessline 434
    |                            | 32 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct-bnb-4bit)            |
  • Network accessline 435
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct-bnb-4bit)            |
  • Network accessline 436
    | **Qwen 2.5 Coder (128 K)** | 0.5 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit)     |
  • Network accessline 437
    |                            | 1.5 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K-GGUF) | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit)     |
  • Network accessline 438
    |                            | 3 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K-GGUF)   | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit)       |
  • Network accessline 439
    |                            | 7 B        | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K-GGUF)   | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit)       |
  • Network accessline 440
    |                            | 14 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K-GGUF)  | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit)      |
  • Network accessline 441
    |                            | 32 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K-GGUF)  | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit)      |
  • Network accessline 442
    | **QwQ**                    | 32 B       | [link](https://huggingface.co/unsloth/QwQ-32B-GGUF)                          | [link](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit)                 |
  • Network accessline 443
    | **QVQ (preview)**          | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/QVQ-72B-Preview-bnb-4bit)                 |
  • Network accessline 444
    | **Qwen 2 (chat)**          | 1.5 B      | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct-bnb-4bit)             |
  • Network accessline 445
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct-bnb-4bit)               |
  • Network accessline 446
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct-bnb-4bit)              |
  • Network accessline 447
    | **Qwen 2 VL**              | 2 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit)    |
  • Network accessline 448
    |                            | 7 B        | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct-unsloth-bnb-4bit)    |
  • Network accessline 449
    |                            | 72 B       | —                                                                            | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct-bnb-4bit)           |
  • Network accessline 451
    <table><thead><tr><th width="174">Model</th><th>Variant</th><th>GGUF</th><th>Instruct (4-bit)</th></tr></thead><tbody><tr><td><strong>Mistral Small</strong></td><td>3.2-24 B (2506)</td><td><a href="https://huggingface.co/unsloth/Mistral-Sma
  • Network accessline 453
    | Model       | Variant          | GGUF                                                             | Instruct (4-bit)                                                             | | ----------- | ---------------- | ------------------------
  • Network accessline 454
    | ----------- | ---------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus   | [link](https://huggingfa
  • Network accessline 455
    | **Phi-4**   | Reasoning-plus   | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus-unsloth-bnb-4bit) |
  • Network accessline 456
    |             | Reasoning        | [link](https://huggingface.co/unsloth/Phi-4-reasoning-GGUF)      | [link](https://huggingface.co/unsloth/phi-4-reasoning-unsloth-bnb-4bit)      |
  • Network accessline 457
    |             | Mini-Reasoning   | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-GGUF) | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit) |
  • Network accessline 458
    |             | Phi-4 (instruct) | [link](https://huggingface.co/unsloth/phi-4-GGUF)                | [link](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit)                |
  • Network accessline 459
    |             | mini (instruct)  | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-GGUF)  | [link](https://huggingface.co/unsloth/Phi-4-mini-instruct-unsloth-bnb-4bit)  |
  • Network accessline 460
    | **Phi-3.5** | mini             | —                                                                | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct-bnb-4bit)        |
  • Network accessline 461
    | **Phi-3**   | mini             | —                                                                | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct-bnb-4bit)       |
  • Network accessline 462
    |             | medium           | —                                                                | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct-bnb-4bit)     |
  • Network accessline 466
    | Model          | Variant           | GGUF                                                                           | Instruct (4-bit)                                                          | | -------------- | ----------------- | -----
  • Network accessline 467
    | -------------- | ----------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | GLM            | 4.5-Air           | [link
  • Network accessline 468
    | GLM            | 4.5-Air           | [link](https://huggingface.co/unsloth/GLM-4.5-Air-GGUF)                        |                                                                           |
  • Network accessline 469
    |                | 4.5               | [4.5](https://huggingface.co/unsloth/GLM-4.5-GGUF)                             |                                                                           |
  • Network accessline 470
    |                | 4-32B-0414        | [4-32B-0414](https://huggingface.co/unsloth/GLM-4-32B-0414-GGUF)               |                                                                           |
  • Network accessline 471
    | Hunyuan        | A13B              | [link](https://huggingface.co/unsloth/Hunyuan-A13B-Instruct-GGUF)              | —                                                                         |
  • Network accessline 472
    | Orpheus        | 0.1-ft (3B)       | [link](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit) |
  • Network accessline 473
    | **LLava**      | 1.5 (7 B)         | —                                                                              | [link](https://huggingface.co/unsloth/llava-1.5-7b-hf-bnb-4bit)           |
  • Network accessline 474
    |                | 1.6 Mistral (7 B) | —                                                                              | [link](https://huggingface.co/unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit)  |
  • Network accessline 475
    | **TinyLlama**  | Chat              | —                                                                              | [link](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit)            |
  • Network accessline 476
    | **SmolLM 2**   | 135 M             | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-bnb-4bit)     |
  • Network accessline 477
    |                | 360 M             | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-360M-Instruct-bnb-4bit)     |
  • Network accessline 478
    |                | 1.7 B             | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-GGUF)              | [link](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct-bnb-4bit)     |
  • Network accessline 479
    | **Zephyr-SFT** | 7 B               | —                                                                              | [link](https://huggingface.co/unsloth/zephyr-sft-bnb-4bit)                |
  • Network accessline 480
    | **Yi**         | 6 B (v1.5)        | —                                                                              | [link](https://huggingface.co/unsloth/Yi-1.5-6B-bnb-4bit)                 |
  • Network accessline 481
    |                | 6 B (v1.0)        | —                                                                              | [link](https://huggingface.co/unsloth/yi-6b-bnb-4bit)                     |
  • Network accessline 482
    |                | 34 B (chat)       | —                                                                              | [link](https://huggingface.co/unsloth/yi-34b-chat-bnb-4bit)               |
  • Network accessline 483
    |                | 34 B (base)       | —                                                                              | [link](https://huggingface.co/unsloth/yi-34b-bnb-4bit)                    |
  • Network accessline 489
    | Model                | Variant                | Instruct (16-bit)                                                          | | -------------------- | ---------------------- | ---------------------------------------------------------------
  • Network accessline 490
    | -------------------- | ---------------------- | -------------------------------------------------------------------------- | | **gpt-oss** (new)    | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b)
  • Network accessline 491
    | **gpt-oss** (new)    | 20b                    | [link](https://huggingface.co/unsloth/gpt-oss-20b)                         |
  • Network accessline 492
    |                      | 120b                   | [link](https://huggingface.co/unsloth/gpt-oss-120b)                        |
  • Network accessline 493
    | **Gemma 3n**         | E2B                    | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it)                     |
  • Network accessline 494
    |                      | E4B                    | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it)                     |
  • Network accessline 495
    | **DeepSeek-R1-0528** | R1-0528-Qwen3-8B       | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B)           |
  • Network accessline 496
    |                      | R1-0528                | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528)                    |
  • Network accessline 497
    | **Mistral**          | Small 3.2 24B (2506)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506) |
  • Network accessline 498
    |                      | Small 3.1 24B (2503)   | [link](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503) |
  • Network accessline 499
    |                      | Small 3.0 24B (2501)   | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501)     |
  • Network accessline 500
    |                      | Magistral Small (2506) | [link](https://huggingface.co/unsloth/Magistral-Small-2506)                |
  • Network accessline 501
    | **Qwen 3**           | 0.6 B                  | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                          |
  • Network accessline 502
    |                      | 1.7 B                  | [link](https://huggingface.co/unsloth/Qwen3-1.7B)                          |
  • Network accessline 503
    |                      | 4 B                    | [link](https://huggingface.co/unsloth/Qwen3-4B)                            |
  • Network accessline 504
    |                      | 8 B                    | [link](https://huggingface.co/unsloth/Qwen3-8B)                            |
  • Network accessline 505
    |                      | 14 B                   | [link](https://huggingface.co/unsloth/Qwen3-14B)                           |
  • Network accessline 506
    |                      | 30B-A3B                | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B)                       |
  • Network accessline 507
    |                      | 32 B                   | [link](https://huggingface.co/unsloth/Qwen3-32B)                           |
  • Network accessline 508
    |                      | 235B-A22B              | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B)                     |
  • Network accessline 509
    | **Llama 4**          | Scout 17B-16E          | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)      |
  • Network accessline 510
    |                      | Maverick 17B-128E      | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct)  |
  • Network accessline 511
    | **Qwen 2.5 Omni**    | 3 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B)                     |
  • Network accessline 512
    |                      | 7 B                    | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B)                     |
  • Network accessline 513
    | **Phi-4**            | Reasoning-plus         | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)                |
  • Network accessline 514
    |                      | Reasoning              | [link](https://huggingface.co/unsloth/Phi-4-reasoning)                     |
  • Network accessline 516
    | Model           | Variant               | Instruct (16-bit)                                                    | | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSe
  • Network accessline 517
    | --------------- | --------------------- | -------------------------------------------------------------------- | | **DeepSeek-V3** | V3-0324               | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324)              | |
  • Network accessline 518
    | **DeepSeek-V3** | V3-0324               | [link](https://huggingface.co/unsloth/DeepSeek-V3-0324)              |
  • Network accessline 519
    |                 | V3                    | [link](https://huggingface.co/unsloth/DeepSeek-V3)                   |
  • Network accessline 520
    | **DeepSeek-R1** | R1-0528               | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528)              |
  • Network accessline 521
    |                 | R1-0528-Qwen3-8B      | [link](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B)     |
  • Network accessline 522
    |                 | R1                    | [link](https://huggingface.co/unsloth/DeepSeek-R1)                   |
  • Network accessline 523
    |                 | R1 Zero               | [link](https://huggingface.co/unsloth/DeepSeek-R1-Zero)              |
  • Network accessline 524
    |                 | Distill Llama 3 8B    | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B)  |
  • Network accessline 525
    |                 | Distill Llama 3.3 70B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B) |
  • Network accessline 526
    |                 | Distill Qwen 2.5 1.5B | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B) |
  • Network accessline 527
    |                 | Distill Qwen 2.5 7B   | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-7B)   |
  • Network accessline 528
    |                 | Distill Qwen 2.5 14B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-14B)  |
  • Network accessline 529
    |                 | Distill Qwen 2.5 32B  | [link](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-32B)  |
  • Network accessline 531
    | Family        | Variant           | Instruct (16-bit)                                                         | | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4*
  • Network accessline 532
    | ------------- | ----------------- | ------------------------------------------------------------------------- | | **Llama 4**   | Scout 17B-16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)     | |
  • Network accessline 533
    | **Llama 4**   | Scout 17B-16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct)     |
  • Network accessline 534
    |               | Maverick 17B-128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct) |
  • Network accessline 535
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B-Instruct)             |
  • Network accessline 536
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct)              |
  • Network accessline 537
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)              |
  • Network accessline 538
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct)      |
  • Network accessline 539
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision-Instruct)      |
  • Network accessline 540
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct)         |
  • Network accessline 541
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-Instruct)        |
  • Network accessline 542
    |               | 405 B             | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-405B-Instruct)       |
  • Network accessline 543
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b-Instruct)                |
  • Network accessline 544
    |               | 70 B              | [link](https://huggingface.co/unsloth/llama-3-70b-Instruct)               |
  • Network accessline 545
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b-chat)                    |
  • Network accessline 547
    | Model        | Variant | Instruct (16-bit)                                      | | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3
  • Network accessline 548
    | ------------ | ------- | ------------------------------------------------------ | | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) | |              | E4B     | [link](https://huggingface.co/unsloth/gemma-3
  • Network accessline 549
    | **Gemma 3n** | E2B     | [link](https://huggingface.co/unsloth/gemma-3n-E4B-it) |
  • Network accessline 550
    |              | E4B     | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it) |
  • Network accessline 551
    | **Gemma 3**  | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-it)   |
  • Network accessline 552
    |              | 4 B     | [link](https://huggingface.co/unsloth/gemma-3-4b-it)   |
  • Network accessline 553
    |              | 12 B    | [link](https://huggingface.co/unsloth/gemma-3-12b-it)  |
  • Network accessline 554
    |              | 27 B    | [link](https://huggingface.co/unsloth/gemma-3-27b-it)  |
  • Network accessline 555
    | **Gemma 2**  | 2 B     | [link](https://huggingface.co/unsloth/gemma-2b-it)     |
  • Network accessline 556
    |              | 9 B     | [link](https://huggingface.co/unsloth/gemma-9b-it)     |
  • Network accessline 557
    |              | 27 B    | [link](https://huggingface.co/unsloth/gemma-27b-it)    |
  • Network accessline 559
    | Family                   | Variant   | Instruct (16-bit)                                                       | | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3
  • Network accessline 560
    | ------------------------ | --------- | ----------------------------------------------------------------------- | | **Qwen 3**               | 0.6 B     | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                       | |
  • Network accessline 561
    | **Qwen 3**               | 0.6 B     | [link](https://huggingface.co/unsloth/Qwen3-0.6B)                       |
  • Network accessline 562
    |                          | 1.7 B     | [link](https://huggingface.co/unsloth/Qwen3-1.7B)                       |
  • Network accessline 563
    |                          | 4 B       | [link](https://huggingface.co/unsloth/Qwen3-4B)                         |
  • Network accessline 564
    |                          | 8 B       | [link](https://huggingface.co/unsloth/Qwen3-8B)                         |
  • Network accessline 565
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen3-14B)                        |
  • Network accessline 566
    |                          | 30B-A3B   | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B)                    |
  • Network accessline 567
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen3-32B)                        |
  • Network accessline 568
    |                          | 235B-A22B | [link](https://huggingface.co/unsloth/Qwen3-235B-A22B)                  |
  • Network accessline 569
    | **Qwen 2.5 Omni**        | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-3B)                  |
  • Network accessline 570
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Omni-7B)                  |
  • Network accessline 571
    | **Qwen 2.5 VL**          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct)           |
  • Network accessline 572
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct)           |
  • Network accessline 573
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-VL-32B-Instruct)          |
  • Network accessline 574
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2.5-VL-72B-Instruct)          |
  • Network accessline 575
    | **Qwen 2.5**             | 0.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct)            |
  • Network accessline 576
    |                          | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct)            |
  • Network accessline 577
    |                          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct)              |
  • Network accessline 578
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct)              |
  • Network accessline 579
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct)             |
  • Network accessline 580
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct)             |
  • Network accessline 581
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct)             |
  • Network accessline 582
    | **Qwen 2.5 Coder 128 K** | 0.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-0.5B-Instruct-128K) |
  • Network accessline 583
    |                          | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-128K) |
  • Network accessline 584
    |                          | 3 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct-128K)   |
  • Network accessline 585
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-128K)   |
  • Network accessline 586
    |                          | 14 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-14B-Instruct-128K)  |
  • Network accessline 587
    |                          | 32 B      | [link](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct-128K)  |
  • Network accessline 588
    | **QwQ**                  | 32 B      | [link](https://huggingface.co/unsloth/QwQ-32B)                          |
  • Network accessline 589
    | **QVQ (preview)**        | 72 B      | —                                                                       | | **Qwen 2 (Chat)**        | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct)              | |
  • Network accessline 590
    | **Qwen 2 (Chat)**        | 1.5 B     | [link](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct)              |
  • Network accessline 591
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2-7B-Instruct)                |
  • Network accessline 592
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2-72B-Instruct)               |
  • Network accessline 593
    | **Qwen 2 VL**            | 2 B       | [link](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct)             |
  • Network accessline 594
    |                          | 7 B       | [link](https://huggingface.co/unsloth/Qwen2-VL-7B-Instruct)             |
  • Network accessline 595
    |                          | 72 B      | [link](https://huggingface.co/unsloth/Qwen2-VL-72B-Instruct)            |
  • Network accessline 597
    | Model            | Variant        | Instruct (16-bit)                                                  | | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral**      | Small
  • Network accessline 598
    | ---------------- | -------------- | ------------------------------------------------------------------ | | **Mistral**      | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) | | **Mistral**      | Large
  • Network accessline 599
    | **Mistral**      | Small 2409-22B | [link](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) |
  • Network accessline 600
    | **Mistral**      | Large 2407     | [link](https://huggingface.co/unsloth/Mistral-Large-Instruct-2407) |
  • Network accessline 601
    | **Mistral**      | 7B v0.3        | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3)    |
  • Network accessline 602
    | **Mistral**      | 7B v0.2        | [link](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2)    |
  • Network accessline 603
    | **Pixtral**      | 12B 2409       | [link](https://huggingface.co/unsloth/Pixtral-12B-2409)            |
  • Network accessline 604
    | **Mixtral**      | 8×7B           | [link](https://huggingface.co/unsloth/Mixtral-8x7B-Instruct-v0.1)  |
  • Network accessline 605
    | **Mistral NeMo** | 12B 2407       | [link](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407)  |
  • Network accessline 606
    | **Devstral**     | Small 2505     | [link](https://huggingface.co/unsloth/Devstral-Small-2505)         |
  • Network accessline 608
    | Model       | Variant        | Instruct (16-bit)                                               | | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus | [link](ht
  • Network accessline 609
    | ----------- | -------------- | --------------------------------------------------------------- | | **Phi-4**   | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)     | |             | Reasoning      | [link](ht
  • Network accessline 610
    | **Phi-4**   | Reasoning-plus | [link](https://huggingface.co/unsloth/Phi-4-reasoning-plus)     |
  • Network accessline 611
    |             | Reasoning      | [link](https://huggingface.co/unsloth/Phi-4-reasoning)          |
  • Network accessline 612
    |             | Phi-4 (core)   | [link](https://huggingface.co/unsloth/Phi-4)                    |
  • Network accessline 613
    |             | Mini-Reasoning | [link](https://huggingface.co/unsloth/Phi-4-mini-reasoning)     |
  • Network accessline 614
    |             | Mini           | [link](https://huggingface.co/unsloth/Phi-4-mini)               |
  • Network accessline 615
    | **Phi-3.5** | Mini           | [link](https://huggingface.co/unsloth/Phi-3.5-mini-instruct)    |
  • Network accessline 616
    | **Phi-3**   | Mini           | [link](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct)   |
  • Network accessline 617
    |             | Medium         | [link](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) |
  • Network accessline 621
    | Model                  | Instruct (16-bit)                                                | | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft)   | [link](https://huggingfac
  • Network accessline 622
    | ---------------------- | ---------------------------------------------------------------- | | Orpheus-3B (v0.1 ft)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft)         | | Orpheus-3B (v0.1 pt)   | [link](https://huggingfac
  • Network accessline 623
    | Orpheus-3B (v0.1 ft)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-ft)         |
  • Network accessline 624
    | Orpheus-3B (v0.1 pt)   | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) |
  • Network accessline 625
    | Sesame-CSM 1B          | [link](https://huggingface.co/unsloth/csm-1b)                    |
  • Network accessline 626
    | Whisper Large V3 (STT) | [link](https://huggingface.co/unsloth/whisper-large-v3)          |
  • Network accessline 627
    | Llasa-TTS 1B           | [link](https://huggingface.co/unsloth/Llasa-1B)                  |
  • Network accessline 628
    | Spark-TTS 0.5B         | [link](https://huggingface.co/unsloth/Spark-TTS-0.5B)            |
  • Network accessline 629
    | Oute-TTS 1B            | [link](https://huggingface.co/unsloth/Llama-OuteTTS-1.0-1B)      |
  • Network accessline 635
    | Model        | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                                           | | ------------ | ----------------- | ----------
  • Network accessline 636
    | ------------ | ----------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | **Gemma 3n** | E2B               | [link](htt
  • Network accessline 637
    | **Gemma 3n** | E2B               | [link](https://huggingface.co/unsloth/gemma-3n-E2B)              | [link](https://huggingface.co/unsloth/gemma-3n-E2B-unsloth-bnb-4bit)                   |
  • Network accessline 638
    |              | E4B               | [link](https://huggingface.co/unsloth/gemma-3n-E4B)              | [link](https://huggingface.co/unsloth/gemma-3n-E4B-unsloth-bnb-4bit)                   |
  • Network accessline 639
    | **Qwen 3**   | 0.6 B             | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base)           | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit)                |
  • Network accessline 640
    |              | 1.7 B             | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base)           | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit)                |
  • Network accessline 641
    |              | 4 B               | [link](https://huggingface.co/unsloth/Qwen3-4B-Base)             | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit)                  |
  • Network accessline 642
    |              | 8 B               | [link](https://huggingface.co/unsloth/Qwen3-8B-Base)             | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit)                  |
  • Network accessline 643
    |              | 14 B              | [link](https://huggingface.co/unsloth/Qwen3-14B-Base)            | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit)                 |
  • Network accessline 644
    |              | 30B-A3B           | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base)        | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-bnb-4bit)                     |
  • Network accessline 645
    | **Llama 4**  | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit) |
  • Network accessline 646
    |              | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                                                      |
  • Network accessline 650
    | Model         | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                | | ------------- | ----------------- | -----------------------------------
  • Network accessline 651
    | ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unslo
  • Network accessline 652
    | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | —                                                           |
  • Network accessline 653
    |               | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                           |
  • Network accessline 654
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B)             | —                                                           |
  • Network accessline 655
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B)              | —                                                           |
  • Network accessline 656
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B)              | —                                                           |
  • Network accessline 657
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision)      | —                                                           |
  • Network accessline 658
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision)      | —                                                           |
  • Network accessline 659
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B)         | —                                                           |
  • Network accessline 660
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B)        | —                                                           |
  • Network accessline 661
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b)                | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)  |
  • Network accessline 662
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b)                | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit)  |
  • Network accessline 663
    |               | 13 B              | [link](https://huggingface.co/unsloth/llama-2-13b)               | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) |
  • Network accessline 665
    | Model        | Variant | Base (16-bit)                                             | Base (4-bit)                                                               | | ------------ | ------- | -------------------------------------------------
  • Network accessline 666
    | ------------ | ------- | --------------------------------------------------------- | -------------------------------------------------------------------------- | | **Qwen 3**   | 0.6 B   | [link](https://huggingface.co/unsloth/Qwen3-0.6B-
  • Network accessline 667
    | **Qwen 3**   | 0.6 B   | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base)    | [link](https://huggingface.co/unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit)    |
  • Network accessline 668
    |              | 1.7 B   | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base)    | [link](https://huggingface.co/unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit)    |
  • Network accessline 669
    |              | 4 B     | [link](https://huggingface.co/unsloth/Qwen3-4B-Base)      | [link](https://huggingface.co/unsloth/Qwen3-4B-Base-unsloth-bnb-4bit)      |
  • Network accessline 670
    |              | 8 B     | [link](https://huggingface.co/unsloth/Qwen3-8B-Base)      | [link](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit)      |
  • Network accessline 671
    |              | 14 B    | [link](https://huggingface.co/unsloth/Qwen3-14B-Base)     | [link](https://huggingface.co/unsloth/Qwen3-14B-Base-unsloth-bnb-4bit)     |
  • Network accessline 672
    |              | 30B-A3B | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base) | [link](https://huggingface.co/unsloth/Qwen3-30B-A3B-Base-unsloth-bnb-4bit) |
  • Network accessline 673
    | **Qwen 2.5** | 0.5 B   | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B)       | [link](https://huggingface.co/unsloth/Qwen2.5-0.5B-bnb-4bit)               |
  • Network accessline 674
    |              | 1.5 B   | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B)       | [link](https://huggingface.co/unsloth/Qwen2.5-1.5B-bnb-4bit)               |
  • Network accessline 675
    |              | 3 B     | [link](https://huggingface.co/unsloth/Qwen2.5-3B)         | [link](https://huggingface.co/unsloth/Qwen2.5-3B-bnb-4bit)                 |
  • Network accessline 676
    |              | 7 B     | [link](https://huggingface.co/unsloth/Qwen2.5-7B)         | [link](https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit)                 |
  • Network accessline 677
    |              | 14 B    | [link](https://huggingface.co/unsloth/Qwen2.5-14B)        | [link](https://huggingface.co/unsloth/Qwen2.5-14B-bnb-4bit)                |
  • Network accessline 678
    |              | 32 B    | [link](https://huggingface.co/unsloth/Qwen2.5-32B)        | [link](https://huggingface.co/unsloth/Qwen2.5-32B-bnb-4bit)                |
  • Network accessline 679
    |              | 72 B    | [link](https://huggingface.co/unsloth/Qwen2.5-72B)        | [link](https://huggingface.co/unsloth/Qwen2.5-72B-bnb-4bit)                |
  • Network accessline 680
    | **Qwen 2**   | 1.5 B   | [link](https://huggingface.co/unsloth/Qwen2-1.5B)         | [link](https://huggingface.co/unsloth/Qwen2-1.5B-bnb-4bit)                 |
  • Network accessline 681
    |              | 7 B     | [link](https://huggingface.co/unsloth/Qwen2-7B)           | [link](https://huggingface.co/unsloth/Qwen2-7B-bnb-4bit)                   |
  • Network accessline 685
    | Model         | Variant           | Base (16-bit)                                                    | Base (4-bit)                                                | | ------------- | ----------------- | -----------------------------------
  • Network accessline 686
    | ------------- | ----------------- | ---------------------------------------------------------------- | ----------------------------------------------------------- | | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unslo
  • Network accessline 687
    | **Llama 4**   | Scout 17B 16E     | [link](https://huggingface.co/unsloth/Llama-4-Scout-17B-16E)     | —                                                           |
  • Network accessline 688
    |               | Maverick 17B 128E | [link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E) | —                                                           |
  • Network accessline 689
    | **Llama 3.3** | 70 B              | [link](https://huggingface.co/unsloth/Llama-3.3-70B)             | —                                                           |
  • Network accessline 690
    | **Llama 3.2** | 1 B               | [link](https://huggingface.co/unsloth/Llama-3.2-1B)              | —                                                           |
  • Network accessline 691
    |               | 3 B               | [link](https://huggingface.co/unsloth/Llama-3.2-3B)              | —                                                           |
  • Network accessline 692
    |               | 11 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-11B-Vision)      | —                                                           |
  • Network accessline 693
    |               | 90 B Vision       | [link](https://huggingface.co/unsloth/Llama-3.2-90B-Vision)      | —                                                           |
  • Network accessline 694
    | **Llama 3.1** | 8 B               | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-8B)         | —                                                           |
  • Network accessline 695
    |               | 70 B              | [link](https://huggingface.co/unsloth/Meta-Llama-3.1-70B)        | —                                                           |
  • Network accessline 696
    | **Llama 3**   | 8 B               | [link](https://huggingface.co/unsloth/llama-3-8b)                | [link](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)  |
  • Network accessline 697
    | **Llama 2**   | 7 B               | [link](https://huggingface.co/unsloth/llama-2-7b)                | [link](https://huggingface.co/unsloth/llama-2-7b-bnb-4bit)  |
  • Network accessline 698
    |               | 13 B              | [link](https://huggingface.co/unsloth/llama-2-13b)               | [link](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) |
  • Network accessline 700
    | Model       | Variant | Base (16-bit)                                         | Base (4-bit)                                                           | | ----------- | ------- | ----------------------------------------------------- | ---
  • Network accessline 701
    | ----------- | ------- | ----------------------------------------------------- | ---------------------------------------------------------------------- | | **Gemma 3** | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-pt)  | [li
  • Network accessline 702
    | **Gemma 3** | 1 B     | [link](https://huggingface.co/unsloth/gemma-3-1b-pt)  | [link](https://huggingface.co/unsloth/gemma-3-1b-pt-unsloth-bnb-4bit)  |
  • Network accessline 703
    |             | 4 B     | [link](https://huggingface.co/unsloth/gemma-3-4b-pt)  | [link](https://huggingface.co/unsloth/gemma-3-4b-pt-unsloth-bnb-4bit)  |
  • Network accessline 704
    |             | 12 B    | [link](https://huggingface.co/unsloth/gemma-3-12b-pt) | [link](https://huggingface.co/unsloth/gemma-3-12b-pt-unsloth-bnb-4bit) |
  • Network accessline 705
    |             | 27 B    | [link](https://huggingface.co/unsloth/gemma-3-27b-pt) | [link](https://huggingface.co/unsloth/gemma-3-27b-pt-unsloth-bnb-4bit) |
  • Network accessline 706
    | **Gemma 2** | 2 B     | [link](https://huggingface.co/unsloth/gemma-2-2b)     | —                                                                      |
  • Network accessline 707
    |             | 9 B     | [link](https://huggingface.co/unsloth/gemma-2-9b)     | —                                                                      |
  • Network accessline 708
    |             | 27 B    | [link](https://huggingface.co/unsloth/gemma-2-27b)    | —                                                                      |
  • Network accessline 712
    | Model       | Variant          | Base (16-bit)                                                      | Base (4-bit)                                                    | | ----------- | ---------------- | -----------------------------------
  • Network accessline 713
    | ----------- | ---------------- | ------------------------------------------------------------------ | --------------------------------------------------------------- | | **Mistral** | Small 24B 2501   | [link](https://huggingface.co/unslo
  • Network accessline 714
    | **Mistral** | Small 24B 2501   | [link](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) | —                                                               |
  • Network accessline 715
    |             | NeMo 12B 2407    | [link](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407)      | —                                                               |
  • Network accessline 716
    |             | 7B v0.3          | [link](https://huggingface.co/unsloth/mistral-7b-v0.3)             | [link](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) |
  • Network accessline 717
    |             | 7B v0.2          | [link](https://huggingface.co/unsloth/mistral-7b-v0.2)             | [link](https://huggingface.co/unsloth/mistral-7b-v0.2-bnb-4bit) |
  • Network accessline 718
    |             | Pixtral 12B 2409 | [link](https://huggingface.co/unsloth/Pixtral-12B-Base-2409)       | —                                                               |
  • Network accessline 722
    | Model          | Variant        | Base (16-bit)                                                    | Base (4-bit)                                                                      | | -------------- | -------------- | -----------------
  • Network accessline 723
    | -------------- | -------------- | ---------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **TinyLlama**  | 1.1 B (Base)   | [link](https://hu
  • Network accessline 724
    | **TinyLlama**  | 1.1 B (Base)   | [link](https://huggingface.co/unsloth/tinyllama)                 | [link](https://huggingface.co/unsloth/tinyllama-bnb-4bit)                         |
  • Network accessline 725
    | **Orpheus-3b** | 0.1-pretrained | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained) | [link](https://huggingface.co/unsloth/orpheus-3b-0.1-pretrained-unsloth-bnb-4bit) |
  • Network accessline 749
    Docker might be the easiest way for Windows users to get started with Unsloth as there is no setup needed or dependency issues. [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell
  • Network accessline 751
    For installation instructions, please follow our [Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker), otherwise here is a quickstart guide:
  • Network accessline 758
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other). Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/ins
  • Dangerous shell operationline 761
    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
  • Network accessline 770
    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image.
  • Network accessline 774
    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. Access the `unsloth-notebooks` tabs to see Unsloth notebooks.
  • Network accessline 779
    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
  • Network accessline 793
    You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx)
  • Network accessline 801
    * Launch the Installer here:  [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/)
  • Network accessline 825
    Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
  • Network accessline 827
    Then install Miniconda (which has Python) here: [https://www.anaconda.com/docs/getting-started/miniconda/install](https://www.anaconda.com/docs/getting-started/miniconda/install#quickstart-install-instructions)
  • Network accessline 833
    You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully. [Install PyTorch](https://pytorch.org/get-started/locally/)
  • Network accessline 850
    * Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
  • Network accessline 857
    1. Install `torch` and `triton`. Go to <https://pytorch.org> to install it. For example `pip install torch torchvision torchaudio triton`
  • Network accessline 858
    2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers. 3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succee
  • Network accessline 859
    3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to <https://github.com/facebookresearch/xformers>. Another option is to install `fl
  • Network accessline 860
    4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-m
  • Network accessline 867
    1. **Install NVIDIA CUDA Toolkit**:    * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads).
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    * Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads).
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    * **Note**: No additional setup is required after installation for Unsloth. 2. **Install Microsoft C++ Build Tools**:    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.mic
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    2. **Install Microsoft C++ Build Tools**:    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/).    * During installation, select the **
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    * Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/).
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    * Verify: Open a new terminal and type `cl`. It should show version info. 4. **Install Conda**    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
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    4. **Install Conda**    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)    2. Follow installation instruction from the website
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    1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
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    1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**.
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    1. Install python though [Python's official site](https://www.python.org/downloads/windows/).
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    pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git"
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    {% hint style="info" %} Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> (May 28th 2025 update) to learn on how to run DeepSeek faster and more efficiently!
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    Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> (May 28th 2025 update) to learn on how to run DeepSeek faster and more efficiently!
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    <table data-full-width="true"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Accuracy</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td><strong>173GB</strong></td><td>Ok</td><td><a hr
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    According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324), these are the recommended settings for inference:
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    * <mark style="background-color:blue;">**Temperature of 0.3**</mark> (Maybe 0.0 for coding as [seen here](https://api-docs.deepseek.com/quick_start/parameter_settings))
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . <mark style="background-color:green;">**I recommend using
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    > **Try QAT with our free** [**Qwen3 (4B) notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb)
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    [Credential-related source omitted]
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    Dequantizing back to 16bits simply does the reverse operation by `float16(qW) / a` . Post-training quantization (PTQ) can greatly reduce storage and inference costs, but quite often degrades accuracy when representing high-precision values
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    [Credential-related source omitted]
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    To enable smarter quantization, we collaborated with the [TorchAO](https://github.com/pytorch/ao) team to add **Quantization-Aware Training (QAT)** directly inside of Unsloth - so now you can fine-tune models in Unsloth and then export them
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    [Credential-related source omitted]
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    Qwen released 2507 (July 2025) updates for their [Qwen3](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune) 4B, 30B and 235B models, introducing both "thinking" and "non-thinking" variants. The non-thinking '**Qwen3-30B-A3B-Inst
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    [Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3-2507 models — 2x faster, with 70% less VRAM, and 8
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    **Unsloth** [**Dynamic 2.0**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **GGUFs:**
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    | Model                    | GGUFs to run:                                                                                                                                                 | | ------------------------ | ----------------------
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    | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Qwen3-**4B-2507**        | [Instruct](https://hug
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    | Qwen3-**4B-2507**        | [Instruct](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF) • [Thinking ](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF)              |
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    | Qwen3-**30B-A3B**-2507   | [Instruct](#llama.cpp-run-qwen3-30b-a3b-instruct-2507-tutorial) • [Thinking](https://huggingface.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF)                 |
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    | Qwen3-**235B-A22B**-2507 | [Instruct](https://huggingface.co/unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF) • [Thinking](https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF) |
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    curl -X POST http://localhost:8000/v1/load_lora_adapter \
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    curl -X POST http://localhost:8000/v1/unload_lora_adapter \
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    * E.g. For image-based training, select a vision model such as *Llama 3.2 Vision*. For code datasets, opt for a specialized model like *Qwen Coder 2.5*. * **Licensing and Requirements**: Different models may have specific licensing terms an
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    * **Licensing and Requirements**: Different models may have specific licensing terms and [system requirements](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#system-requirements). Be sure to review these carefully to avoid
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    * Use our [VRAM guideline](https://docs.unsloth.ai/beginner-start-here/unsloth-requirements#approximate-vram-requirements-based-on-model-parameters) to determine the VRAM requirements for the model you’re considering.
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    * We recommend using the latest model for the best performance and capabilities. For instance, as of January 2025, the leading 70B model is *Llama 3.3*. * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-
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    * You can stay up to date by exploring our [model catalog](https://docs.unsloth.ai/get-started/all-our-models) to find the newest and relevant options.
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    Base models, on the other hand, are the original pre-trained versions without instruction fine-tuning. These are specifically designed for customization through fine-tuning, allowing you to adapt them to your unique needs. Base models are c
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    * **300–1,000 Rows of High-Quality Data**: With a medium-sized, high-quality dataset, fine-tuning the base or instruct model are both viable options. * **Less than 300 Rows**: For smaller datasets, the instruct model is typically the better
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    * For information how how big your dataset should be, [see here](https://docs.unsloth.ai/get-started/datasets-guide#how-big-should-my-dataset-be)
  • Network accessline 1481
    * Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi
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    [Credential-related source omitted]
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    * You can use our [Magistral-2509 Kaggle notebook](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/magistral-how-to-run-and-fine-tune#fine-tuning-magistral-with-unsloth) as an example which utilizes multi-GPU Unsloth
  • Network accessline 1551
    * [unsloth-5090-multiple](https://github.com/thad0ctor/unsloth-5090-multiple): A fork enabling Unsloth to run efficiently on multi-GPU systems, particularly for the NVIDIA [RTX 5090](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blac
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    * [opensloth](https://github.com/anhvth/opensloth): Unsloth with support for multi-GPU training including experimental features.
  • Network accessline 1554
    **Stay tuned for our official announcement!**\ For more details, check out our ongoing [Pull Request](https://github.com/unslothai/unsloth/issues/2435) discussing multi-GPU support.
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    For more details, check out our ongoing [Pull Request](https://github.com/unslothai/unsloth/issues/2435) discussing multi-GPU support.
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    If you're a beginner, here might be the first questions you'll ask before your first fine-tune. You can also always ask our community by joining our [Reddit page](https://www.reddit.com/r/unsloth/).
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    [Credential-related source omitted]
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    uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
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    Unsloth supports natively 2x faster inference. For our inference only notebook, click [here](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing).
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    Sometimes when you execute a cell [this error](https://github.com/googlecolab/colabtools/issues/3409) can appear. To solve this, in a new cell, run the below:
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    {% hint style="success" %} Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> for an updated DeepSeek R1-0528 (May 28th 2025 version)
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    Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> for an updated DeepSeek R1-0528 (May 28th 2025 version)
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    1. Do not forget about `<｜User｜>` and `<｜Assistant｜>` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at: [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp). You can follow the build instruc
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    2. Obtain the latest `llama.cpp` at: [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp). You can follow the build instructions below as well:
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggerganov/llama.cpp
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    This means the memory space for inference and training can in theory be re-used, since inference and training are separate modes - this is where [vLLM's sleep mode feature](https://docs.vllm.ai/en/latest/features/sleep_mode.html#rlhf-weight
  • Network accessline 1828
    [Credential-related source omitted]
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    [Credential-related source omitted]
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    <table data-full-width="true"><thead><tr><th>Experiments</th><th>Config</th><th>Status</th><th>GPU Memory usage</th><th>Comments</th></tr></thead><tbody><tr><td><ol><li><a href="https://colab.research.google.com/drive/18CssBY5C0mStnLvu2Hlt4
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    We now select better compilation flags and reduce compile times by 50% or more. We also managed to dynamically patch any vLLM version to handle `gc.collect` better for backwards compatibility reasons, as inspired from this [vLLM pull reques
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    All our GRPO notebooks have Unsloth Standby on by default and all optimizations! See <https://docs.unsloth.ai/get-started/unsloth-notebooks> for all our GRPO notebooks, or try the below:
  • Network accessline 1868
    * [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA
  • Network accessline 1869
    * [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecases)
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    * [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)
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    * [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA
  • Network accessline 1872
    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb)
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    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)
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    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)
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    * [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb)
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    {% hint style="warning" %} If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better rew
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    If you're having issues with your GRPO model not learning, we'd highly recommend to use our [Advanced GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-notebooks) as it has a much better reward function and you should
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    You can then save your model to GGUF, Ollama etc. by following our [guide here](https://docs.unsloth.ai/fine-tuning-llms-guide#id-7.-running--saving-the-model).
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    [Credential-related source omitted]
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    We have multiple options for saving your fine-tuned model, but we’ll focus on the easiest and most popular approaches which you can read more about [here](https://docs.unsloth.ai/basics/running-and-saving-models)
  • Network accessline 2095
    Install the latest PyTorch, TorchAO, Xformers from <https://pytorch.org/>
  • Network accessline 2113
    And that's it! Try some examples in our [**Unsloth Notebooks**](https://docs.unsloth.ai/get-started/unsloth-notebooks) page!
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    You can use our :ledger:[gpt-oss RL auto win 2048](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game_BF16.ipynb) example on a MI300X (192GB) GPU. The goal is to play the 2048 game automatic
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    You can also use our :ledger:[automatic kernel gen RL notebook](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_GRPO_BF16.ipynb) also with gpt-oss to auto create matrix multiplication kernels in Python. The notebook also
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    [Credential-related source omitted]
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    AMD GPUs also need the bitsandbytes `blocksize` to be 128 and not 64 - this also means our pre-quantized models (for example [unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit)) fr
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    pip install --upgrade torch==2.8.0 pytorch-triton-rocm torchvision torchaudio torchao==0.13.0 xformers --index-url https://download.pytorch.org/whl/rocm6.4
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    pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git
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    pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git
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    pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth"
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    [Credential-related source omitted]
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    bash NOTEBOOK_URL="https://raw.githubusercontent.com/unslothai/notebooks/refs/heads/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb" wget -O "gpt_oss_20B_RL_2048_Game.ipynb" "$NOTEBOOK_URL"
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    NOTEBOOK_URL="https://raw.githubusercontent.com/unslothai/notebooks/refs/heads/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb"
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    wget -O "gpt_oss_20B_RL_2048_Game.ipynb" "$NOTEBOOK_URL"
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    [Credential-related source omitted]
  • Network accessline 2437
    Don't forget Unsloth also allows you to [save and run](https://docs.unsloth.ai/basics/running-and-saving-models) your models after fine-tuning so you can locally deploy them directly on your DGX Spark after.
  • Network accessline 2441
    Many thanks to [Lakshmi Ramesh](https://www.linkedin.com/in/rlakshmi24/) and [Barath Anandan](https://www.linkedin.com/in/barathsa/) from NVIDIA for helping Unsloth’s DGX Spark launch and building the Docker image.
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    [Credential-related source omitted]
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    Thanks to Tim from [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) for providing a great fine-tuning tutorial with Unsloth on DGX Spark:
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    {% embed url="<https://www.youtube.com/watch?t=962s&v=zs-J9sKxvoM>" %}
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    [Credential-related source omitted]
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    Inside the container, start Jupyter and run the required notebook. You can use the Reinforcement Learning gpt-oss 20b to win 2048 [notebook here](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_204
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    [Credential-related source omitted]
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    </strong>    "unsloth/gpt-oss-20b", # 20B model using MXFP4 format     "unsloth/gpt-oss-120b", ] # More models at https://huggingface.co/unsloth
  • Network accessline 2506
    ] # More models at https://huggingface.co/unsloth
  • Network accessline 2523
    Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
  • Network accessline 2525
    {% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.
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    To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.&#x20;
  • Network accessline 2531
    For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated
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    [Credential-related source omitted]
  • Network accessline 2543
    One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` ,
  • Network accessline 2545
    {% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 2546
    🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 2549
    Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide).
  • Network accessline 2552
    We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet
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    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Network accessline 2586
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 2599
    As always, feel free to drop by our [Discord](https://discord.com/invite/unsloth) or [Reddit](https://www.reddit.com/r/unsloth/) if you need any help.
  • Network accessline 2605
    Yes you can now [save/export your gpt-oss fine-tuned](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training) model using Unsloth's new update!
  • Network accessline 2617
    Yes! Unsloth now supports RL for gpt-oss with GRPO/GSPO. We made it work on a free Kaggle notebook and achieved the fastest inference for RL. [Read more here](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning)
  • Network accessline 2619
    ***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!*
  • Network accessline 2686
    * The [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb) is for continued pretraining/raw text.
  • Network accessline 2687
    * The [continued pretraining notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb) is for learning another language.
  • Network accessline 2689
    You can read more about continued pretraining and our release in our [blog post](https://unsloth.ai/blog/contpretraining).
  • Network accessline 2996
    1. We find using lower KV cache quantization (4bit) seems to degrade generation quality via empirical tests - more tests need to be done, but we suggest using `q8_0` cache quantization. The goal of quantization is to support longer context
  • Network accessline 2997
    2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dynamic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices. 3. Using `llama.cpp`
  • Network accessline 2998
    3. Using `llama.cpp` 's Flash Attention backend does result in somewhat faster decoding speeds. Use `-DGGML_CUDA_FA_ALL_QUANTS=ON` when compiling. Note it's also best to set your CUDA architecture as found in <https://developer.nvidia.com/c
  • Network accessline 3023
    Kimi-K2-Instruct-0905 the new version of K2 achieves SOTA performance in knowledge, reasoning, coding, and agentic tasks. The full 1T parameter model from Moonshot AI requires 1.09TB of disk space, while the quantized **Unsloth Dynamic 1.8-
  • Network accessline 3025
    You can now run **Kimi-K2-Instruct-0905** with our new GGUFs. Use our same settings below but ensure you change the model name from 'Kimi-K2-Instruct' to 'Kimi-K2-Instruct-0905': [K2-0905 GGUFs](https://huggingface.co/unsloth/Kimi-K2-Instru
  • Network accessline 3027
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized LLMs with minimal accuracy loss.
  • Network accessline 3029
    <a href="https://docs.unsloth.ai/basics/kimi-k2-how-to-run-locally#run-kimi-k2-tutorials" class="button primary">Run in llama.cpp</a>
  • Network accessline 3053
    According to [Moonshot AI](https://huggingface.co/moonshotai/Kimi-K2-Instruct), these are the recommended settings for Kimi K2 inference:
  • Network accessline 3078
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/Kimi-K2-Instruct-GGUF/tree/main/UD-TQ1_0">U
  • Network accessline 3080
    We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/Kimi-K2-Instruct-BF16).
  • Network accessline 3084
    {% hint style="success" %} You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model:
  • Network accessline 3085
    You can now use the latest update of [llama.cpp](https://github.com/ggml-org/llama.cpp) to run the model:
  • Network accessline 3090
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 3106
    3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-TQ1_0`(dynamic 1.8bit quant) or other quantized versions like `Q2_K_XL` . We <mark style="background-color:green;">**recommend using
  • Network accessline 3146
    Also see our GitHub repo for our notebooks: [github.com/unslothai/notebooks](https://github.com/unslothai/notebooks/)
  • Network accessline 3152
    * [**gpt-oss (20b)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) • [Inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)
  • Network accessline 3153
    * [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new**
  • Network accessline 3154
    * [Qwen3 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) • [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL
  • Network accessline 3155
    * [**Qwen3-2507-4B**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507) • [Thinking](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) • [Instruct](https://colab.r
  • Network accessline 3156
    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversation
  • Network accessline 3157
    * [IBM Granite-4.0-H](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) - new
  • Network accessline 3158
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Text](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [Vision](https://colab
  • Network accessline 3159
    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)&#x20;
  • Network accessline 3160
    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Alpaca.ipynb) • [Llama 3.2 (1B + 3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3
  • Network accessline 3164
    * [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) (automatic kernels creation) - new
  • Network accessline 3165
    * [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) (auto win 2048 game) - new
  • Network accessline 3166
    * [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision **GSPO** - new
  • Network accessline 3167
    * [Qwen3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA
  • Network accessline 3168
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 3169
    * [**DeepSeek-R1-0528-Qwen3 (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) (for multilingual usecase)
  • Network accessline 3170
    * [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)
  • Network accessline 3171
    * [Llama 3.2 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - Advanced GRPO LoRA
  • Network accessline 3172
    * [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb)
  • Network accessline 3173
    * [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)&#x20;
  • Network accessline 3174
    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)
  • Network accessline 3178
    * [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) - new
  • Network accessline 3179
    * [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb)
  • Network accessline 3180
    * [Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - Speech-to-Text (STT)
  • Network accessline 3181
    * [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb)
  • Network accessline 3182
    * [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb)
  • Network accessline 3183
    * [Oute-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_\(1B\).ipynb)
  • Network accessline 3187
    * [Whisper-Large-V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb)
  • Network accessline 3188
    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - Audio
  • Network accessline 3192
    * [**Qwen3-VL (8B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb) **- new**
  • Network accessline 3193
    * [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) **- new**
  • Network accessline 3194
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) - vision
  • Network accessline 3195
    * [Gemma 3n (E4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) - vision
  • Network accessline 3196
    * [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb)
  • Network accessline 3197
    * [Qwen2.5-VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb)
  • Network accessline 3198
    * [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb)
  • Network accessline 3199
    * [Qwen3-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 3200
    * [Qwen2.5-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) - Vision GSPO
  • Network accessline 3201
    * [Gemma 3 (4B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - Vision GSPO - new
  • Network accessline 3211
    * [gpt-oss-120b ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(120B\)_A100-Fine-tuning.ipynb)- new
  • Network accessline 3212
    * [Qwen3 (32B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(32B\)_A100-Reasoning-Conversational.ipynb) - new
  • Network accessline 3213
    * [Llama 3.3 (70B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.3_\(70B\)_A100-Conversational.ipynb) - new
  • Network accessline 3214
    * [Gemma 3 (27B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(27B\)_A100-Conversational.ipynb) - new
  • Network accessline 3218
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 3219
    * [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new**
  • Network accessline 3220
    * [**ModernBERT-large**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) **- new** as of Aug 19
  • Network accessline 3221
    * [**Synthetic Data Generation Llama 3.2 (3B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_\(3B\).ipynb) - new
  • Network accessline 3222
    * [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new**
  • Network accessline 3223
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 3224
    * [Mistral v0.3 Instruct (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
  • Network accessline 3225
    * [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 3226
    * [ORPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb)
  • Network accessline 3227
    * [Continued Pretraining](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 3228
    * [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 3229
    * [***Inference only***](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Inference.ipynb)
  • Network accessline 3230
    * [Llama 3 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Alpaca.ipynb)
  • Network accessline 3234
    * [**Customer support agent**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb) **- new**
  • Network accessline 3235
    * [**Automatic Kernel Creation**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) with RL **- new**
  • Network accessline 3236
    * [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 3237
    * [**BERT - Text Classification**](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) **- new as of Aug 19**
  • Network accessline 3238
    * [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 3239
    * [**Tool Calling**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb) **- new**
  • Network accessline 3240
    * [Continued Pretraining (CPT)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 3241
    * [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail
  • Network accessline 3242
    * [KTO](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) by Jeffrey
  • Network accessline 3243
    * [Inference chat UI](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)
  • Network accessline 3244
    * [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 3245
    * [ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
  • Network accessline 3246
    * [Text Completion](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb)
  • Network accessline 3250
    * [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb)
  • Network accessline 3251
    * [Gemma 2 (9B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(9B\)-Alpaca.ipynb)
  • Network accessline 3252
    * [Mistral NeMo (12B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Nemo_\(12B\)-Alpaca.ipynb)
  • Network accessline 3253
    * [Phi-3.5 (mini)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb)
  • Network accessline 3254
    * [Phi-3 (medium)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3_Medium-Conversational.ipynb)
  • Network accessline 3255
    * [Gemma 2 (2B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(2B\)-Alpaca.ipynb)
  • Network accessline 3256
    * [Qwen 2.5 Coder (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(14B\)-Conversational.ipynb)
  • Network accessline 3257
    * [Mistral Small (22B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_\(22B\)-Alpaca.ipynb)
  • Network accessline 3258
    * [TinyLlama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/TinyLlama_\(1.1B\)-Alpaca.ipynb)
  • Network accessline 3259
    * [CodeGemma (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_\(7B\)-Conversational.ipynb)
  • Network accessline 3260
    * [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Alpaca.ipynb)
  • Network accessline 3261
    * [Qwen2 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_\(7B\)-Alpaca.ipynb)
  • Network accessline 3265
    * [**gpt-oss (20B)**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-gpt-oss-\(20B\)-Fine-tuning.ipynb\&accelerator=nvidiaTeslaT4) **- new**
  • Network accessline 3266
    * [Gemma 3n (E4B)](https://www.kaggle.com/code/danielhanchen/gemma-3n-4b-multimodal-finetuning-inference)
  • Network accessline 3267
    * [Qwen3 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(14B\).ipynb)
  • Network accessline 3268
    * [Magistral-2509 (24B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=nvidiaTeslaT4) - new
  • Network accessline 3269
    * [Gemma 3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(4B\).ipynb)
  • Network accessline 3270
    * [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4-Conversational.ipynb)
  • Network accessline 3271
    * [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Alpaca.ipynb)
  • Network accessline 3272
    * [Llama 3.2 (1B + 3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 3273
    * [Qwen 2.5 (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(7B\)-Alpaca.ipynb)
  • Network accessline 3277
    * [**Qwen2.5-VL**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_5_7B_VL_GRPO.ipynb\&accelerator=nvidiaTeslaT4) - Vision GRPO - new
  • Network accessline 3278
    * [Qwen3 (4B)](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(4B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4)
  • Network accessline 3279
    * [Gemma 3 (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(1B\)-GRPO.ipynb)
  • Network accessline 3280
    * [Llama 3.1 (8B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-GRPO.ipynb)
  • Network accessline 3281
    * [Phi-4 (14B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4_\(14B\)-GRPO.ipynb)
  • Network accessline 3282
    * [Qwen 2.5 (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(3B\)-GRPO.ipynb)
  • Network accessline 3286
    * [Sesame-CSM (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Sesame_CSM_\(1B\)-TTS.ipynb)
  • Network accessline 3287
    * [Orpheus-TTS (3B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Orpheus_\(3B\)-TTS.ipynb)
  • Network accessline 3288
    * [Whisper Large V3](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Whisper.ipynb) – Speech-to-Text
  • Network accessline 3289
    * [Llasa-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llasa_TTS_\(1B\).ipynb)
  • Network accessline 3290
    * [Spark-TTS (0.5B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Spark_TTS_\(0_5B\).ipynb)
  • Network accessline 3291
    * [Oute-TTS (1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Oute_TTS_\(1B\).ipynb)
  • Network accessline 3295
    * [Llama 3.2 Vision (11B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(11B\)-Vision.ipynb)
  • Network accessline 3296
    * [Qwen 2.5-VL (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_VL_\(7B\)-Vision.ipynb)
  • Network accessline 3297
    * [Pixtral (12B) 2409](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Pixtral_\(12B\)-Vision.ipynb)
  • Network accessline 3301
    * [Tool Calling](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb\&accelerator=nvidiaTeslaT4)
  • Network accessline 3302
    * [ORPO](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-ORPO.ipynb)
  • Network accessline 3303
    * [Continued Pretraining](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_v0.3_\(7B\)-CPT.ipynb)
  • Network accessline 3304
    * [DPO Zephyr](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 3305
    * [Inference only](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Inference.ipynb)
  • Network accessline 3306
    * [Ollama](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 3307
    * [Text Completion](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_\(7B\)-Text_Completion.ipynb)
  • Network accessline 3308
    * [CodeForces-cot (Reasoning)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb)
  • Network accessline 3309
    * [Unsloth Studio (chat UI)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Unsloth_Studio.ipynb)
  • Network accessline 3313
    * [Gemma 2 (9B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(9B\)-Alpaca.ipynb)
  • Network accessline 3314
    * [Gemma 2 (2B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(2B\)-Alpaca.ipynb)
  • Network accessline 3315
    * [CodeGemma (7B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_\(7B\)-Conversational.ipynb)
  • Network accessline 3316
    * [Mistral NeMo (12B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Nemo_\(12B\)-Alpaca.ipynb)
  • Network accessline 3317
    * [Mistral Small (22B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Small_\(22B\)-Alpaca.ipynb)
  • Network accessline 3318
    * [TinyLlama (1.1B)](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-TinyLlama_\(1.1B\)-Alpaca.ipynb)
  • Network accessline 3320
    To view a complete list of all our Kaggle notebooks, [click here](https://github.com/unslothai/notebooks#-kaggle-notebooks).
  • Network accessline 3322
    {% hint style="info" %} Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)!
  • Network accessline 3323
    Feel free to contribute to the notebooks by visiting our [repo](https://github.com/unslothai/notebooks)!
  • Network accessline 3334
    {% hint style="warning" %} Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install).
  • Network accessline 3335
    Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install).
  • Network accessline 3340
    If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below:
  • Network accessline 3358
    mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
  • Network accessline 3359
    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
  • Dangerous shell operationline 3361
    rm -rf ~/miniconda3/miniconda.sh
  • Network accessline 3392
    You can also run your fine-tuned models by using [Unsloth's 2x faster inference](https://docs.unsloth.ai/basics/running-and-saving-models/unsloth-inference).
  • Network accessline 3404
    Unsloth now supports vision/multimodal RL with [Qwen3-VL](https://docs.unsloth.ai/models/qwen3-vl-how-to-run-and-fine-tune), [Gemma 3](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune) and more. Due to Unsloth's unique [weigh
  • Network accessline 3406
    Unsloth can train Qwen3-VL-8B with GSPO/GRPO on a free Colab T4 GPU. Other VLMs work too, but may need larger GPUs. Gemma requires newer GPUs than T4 because vLLM [restricts to Bfloat16](https://docs.unsloth.ai/models/gemma-3-how-to-run-and
  • Network accessline 3408
    * **Qwen-3 VL-8B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb)
  • Network accessline 3409
    * **Qwen-2.5 VL-7B** (vLLM inference)**:** [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) •[ Kaggle](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/note
  • Network accessline 3410
    * **Gemma-3-4B** (Unsloth inference): [Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb)
  • Network accessline 3412
    We have also added vLLM VLM integration into Unsloth natively, so all you have to do to use vLLM inference is enable the `fast_inference=True` flag when initializing the model. Special thanks to [Sinoué GAD](https://github.com/unslothai/uns
  • Network accessline 3414
    This VLM support also integrates our latest update for even more memory efficient + faster RL including our [Standby feature](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/memory-efficient-rl#unsloth-standby), which un
  • Access to secretsline 3427
    [Credential-related source omitted]
  • Network accessline 3454
    <pre class="language-bash"><code class="lang-bash">pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 3455
    <strong>pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
  • Network accessline 3460
    '2025.1.5' is one of the previous old versions of Unsloth. Change it to a specific release listed on our [Github here](https://github.com/unslothai/unsloth/releases).
  • Access to secretsline 3510
    [Credential-related source omitted]
  • Network accessline 3536
    pip install ninja export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3537
    export TORCH_CUDA_ARCH_LIST="12.0" git clone --depth=1 https://github.com/facebookresearch/xformers --recursive cd xformers && python setup.py install && cd ..
  • Network accessline 3538
    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3541
    uv pip install unsloth bash    curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env
  • Network accessline 3542
    bash    curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env    bash
  • Dangerous shell operationline 3543
    curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env
  • Network accessline 3543
    curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env
  • Network accessline 3552
    bash    uv pip install -qqq \    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 3553
    uv pip install -qqq \    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \    "unsloth[base] @ git+https://github.com/unslothai/unsloth"
  • Network accessline 3554
    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 3555
    "unsloth[base] @ git+https://github.com/unslothai/unsloth"
  • Network accessline 3561
    pip install ninja    export TORCH_CUDA_ARCH_LIST="12.0"    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3562
    export TORCH_CUDA_ARCH_LIST="12.0"    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive    cd xformers && python setup.py install && cd ..
  • Network accessline 3563
    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3566
    uv pip install -U transformers    bash    curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
  • Network accessline 3567
    bash    curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"    bash
  • Network accessline 3568
    curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
  • Network accessline 3574
    conda activate unsloth-blackwell    bash    pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128
  • Network accessline 3575
    bash    pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128    bash
  • Network accessline 3576
    pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128
  • Network accessline 3584
    pip install ninja    export TORCH_CUDA_ARCH_LIST="12.0"    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3585
    export TORCH_CUDA_ARCH_LIST="12.0"    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive    cd xformers && python setup.py install && cd ..
  • Network accessline 3586
    git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
  • Network accessline 3608
    # Install xformers from source with optimized build flags    pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
  • Network accessline 3609
    pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
  • Network accessline 3622
    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate image needed.
  • Network accessline 3624
    For installation instructions, please follow our [Unsloth Docker guide](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker).
  • Network accessline 3664
    Google’s Gemma 3n multimodal model handles image, audio, video, and text inputs. Available in 2B and 4B sizes, it supports 140 languages for text and multimodal tasks. You can now run and fine-tune **Gemma-3n-E4B** and **E2B** locally using
  • Network accessline 3666
    > **Fine-tune Gemma 3n with our** [**free Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb)
  • Network accessline 3674
    <table><thead><tr><th width="249">Dynamic 2.0 GGUF (text only)</th><th width="285">Dynamic 4-bit Instruct (to fine-tune)</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/gemma-3n-E2B-it
  • Network accessline 3676
    **See all our Gemma 3n uploads including base and more formats in** [**our collection here**](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339)**.**
  • Network accessline 3720
    {% hint style="info" %} We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp!
  • Network accessline 3721
    We would first like to thank [Xuan-Son Nguyen](https://x.com/ngxson) from Hugging Face, [Georgi Gerganov](https://x.com/ggerganov) from the llama.cpp team on making Gemma 3N work in llama.cpp!
  • Network accessline 3724
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Dangerous shell operationline 3745
    ollama rm hf.co/unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL
  • Network accessline 3752
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 3754
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 3754
    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 3781
    * It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no
  • Network accessline 3782
    * <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark>   * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/
  • Network accessline 3783
    * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)
  • Network accessline 3784
    * Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb)
  • Network accessline 3785
    * Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 3786
    * Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
  • Network accessline 3787
    * Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
  • Network accessline 3788
    * **More notebooks in our** [**notebooks repo**](https://github.com/unslothai/notebooks)**.**
  • Network accessline 3806
    RUN git clone --depth=1 https://github.com/facebookresearch/xformers --recursive && \
  • Network accessline 3824
    !uv pip install -qqq \     "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 3825
    "torch>=2.8.0" "triton>=3.4.0" {install_numpy} \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \     "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
  • Network accessline 3826
    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
  • Network accessline 3827
    "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
  • Network accessline 3828
    torchvision bitsandbytes \     git+https://github.com/huggingface/transformers \     git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
  • Network accessline 3829
    git+https://github.com/huggingface/transformers \
  • Network accessline 3830
    git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
  • Network accessline 3835
    We’ll load **`gpt-oss-20b`**  using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work for QLoRA fine-tuning). Configur
  • Network accessline 3862
    * [GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-rl-notebooks)
  • Network accessline 3863
    * [ORPO notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb)
  • Network accessline 3864
    * [DPO Zephyr notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
  • Network accessline 3865
    * [KTO notebook](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing)
  • Network accessline 3866
    * [SimPO notebook](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing)
  • Network accessline 3868
    We're also in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#acceler
  • Access to secretsline 3873
    [Credential-related source omitted]
  • Network accessline 3905
    Finetuned from [**Mistral-Small-3.1**](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF), Devstral supports a 128k context window. Devstral Small 1.1 has improved performance, achieving a score of 53.6% performance on
  • Network accessline 3917
    All Devstral uploads use our Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology, delivering the best performance on 5-shot MMLU and KL Divergence benchmarks. This means, you can run and fine-tune qua
  • Network accessline 3921
    | Devstral 2507 (new)                                                                                                    | Devstral 2505                                                                                               | | -----
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    | ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | GGUF:
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    | GGUF: [Devstral-Small-2507-GGUF](https://huggingface.co/unsloth/Devstral-Small-2507-GGUF)                              | [Devstral-Small-2505-GGUF](https://huggingface.co/unsloth/Devstral-Small-2505-GGUF)                         |
  • Network accessline 3924
    | 4-bit BnB: [Devstral-Small-2507-unsloth-bnb-4bit](https://huggingface.co/unsloth/Devstral-Small-2507-unsloth-bnb-4bit) | [Devstral-Small-2505-unsloth-bnb-4bit](https://huggingface.co/unsloth/Devstral-Small-2505-unsloth-bnb-4bit) |
  • Network accessline 3936
    **A system prompt is recommended**, and is a derivative of Open Hand's system prompt. The full system prompt is provided [here](https://huggingface.co/unsloth/Devstral-Small-2505/blob/main/SYSTEM_PROMPT.txt).
  • Network accessline 3946
    2. Run the model with our dynamic quant. Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Devstral supports 128K context
  • Network accessline 3947
    3. Also Devstral supports 128K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves
  • Network accessline 3951
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 3986
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggerganov/llama.cpp
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    RUN git clone https://github.com/triton-lang/triton.git && \
  • Network accessline 4084
    **Not necessarily.** Many assume RAG will consistently outperform a fine-tuned model, but that’s not the case when fine-tuning is done properly. In fact, a well-tuned model often matches or even surpasses RAG-based systems on specialized ta
  • Network accessline 4090
    **Not at all!** While full fine-tuning or pretraining can be costly, these are not necessary (pretraining is especially not necessary). In most cases, LoRA or QLoRA fine-tuning can be done for minimal cost. In fact, with Unsloth’s [free not
  • Network accessline 4107
    We recommend starting with **QLoRA**, as it’s one of the most efficient and accessible methods available. Thanks to Unsloth’s [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss compared to standard 16-bit LoRA f
  • Network accessline 4113
    {% content-ref url="../fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
  • Network accessline 4114
    [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    * Use `USER_PASSWORD` for sudo operations inside container
  • Network accessline 4193
    Read our full DeepSeek-R1 blogpost here: [unsloth.ai/blog/deepseekr1-dynamic](https://unsloth.ai/blog/deepseekr1-dynamic)
  • Network accessline 4209
    {% tabs %} {% tab title="IQ1\_S code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FqpBdpW55h5mNAzVoTxPI%2Finference_UD-IQ1_S_3407.txt?alt=media&toke
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    {% tabs %} {% tab title="IQ1\_S basic code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FFtAMzAucSfKMkkmXItTj%2Finference_basic-IQ1_S_3407.txt?alt=m
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  • Network accessline 4252
    {% tab title="IQ2\_XXS basic code" %} {% file src="<https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FxhOjnexMCB3dmuQFQ2Zq%2Fuploads%2FajsVHsVqlWpwHk7mY32t%2Finference_basic-IQ2_XXS_3407.txt?alt=media&to
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  • Network accessline 4275
    If you're still encountering any issues with versions or dependencies, please use our [Docker image](https://docs.unsloth.ai/get-started/install-and-update/docker) which will have everything pre-installed.
  • Network accessline 4288
    * It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no
  • Network accessline 4289
    * <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark>   * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/
  • Network accessline 4290
    * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)
  • Network accessline 4291
    * Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb)
  • Network accessline 4292
    * Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 4293
    * Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
  • Network accessline 4294
    * Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
  • Network accessline 4295
    * **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks)
  • Network accessline 4320
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 4355
    DeepSeek-R1-0528 is DeepSeek's new update to their R1 reasoning model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic **1.66-bit** version uses 162GB (-80% reduction in size). GGUF: [DeepSeek-R1-0528-GGUF]
  • Network accessline 4357
    DeepSeek also released a R1-0528 distilled version by fine-tuning Qwen3 (8B). The distill achieves similar performance to Qwen3 (235B). ***You can also*** [***fine-tune Qwen3 Distill***](#fine-tuning-deepseek-r1-0528-with-unsloth) ***with U
  • Network accessline 4359
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss.
  • Network accessline 4366
    NEW: Huge improvements to tool calling and chat template fixes.\ \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM
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    \ New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/
  • Network accessline 4368
    New [TQ1\_0 dynamic 1.66-bit quant](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSeek-R1-0528-UD-TQ1_0.gguf) - 162GB in size. Ideal for 192GB RAM (including Mac) and Ollama users. Try: `ollama run hf.co/unsloth/De
  • Network accessline 4387
    According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528), these are the recommended settings for R1 (R1-0528 and Qwen3 distill should use the same settings) inference:
  • Network accessline 4404
    * Qwen3 (8B) distill: [DeepSeek-R1-0528-Qwen3-8B-GGUF](https://huggingface.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF)
  • Network accessline 4407
    We also uploaded [IQ4\_NL](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/IQ4_NL) and [Q4\_1](https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_1) quants which run specifically faster for ARM and Apple device
  • Network accessline 4409
    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type + Link</th><th>Disk Size</th><th>Details</th></tr></thead><tbody><tr><td>1.66bit</td><td><a href="https://huggingface.co/unsloth/DeepSeek-R1-0528-GGUF?show_file_info=DeepSe
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    We've also uploaded versions in [BF16 format](https://huggingface.co/unsloth/DeepSeek-R1-0528-BF16), and original [FP8 (float8) format](https://huggingface.co/unsloth/DeepSeek-R1-0528).
  • Network accessline 4425
    Open WebUI has made an step-by-step tutorial on how to run R1 here and for R1-0528, you will just need to replace R1 with the new 0528 quant: [docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/](https://docs.openwebui.com/tutoria
  • Network accessline 4433
    1. <mark style="background-color:yellow;">**To run the full 720GB R1-0528 model,**</mark> [<mark style="background-color:yellow;">**see here**</mark>](#run-full-r1-0528-on-llama.cpp)<mark style="background-color:yellow;">**.**</mark> Obtain
  • Network accessline 4439
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 4455
    3. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose `UD-IQ1_S`(dynamic 1.78bit quant) or other quantized versions like `Q4_K_M` . We <mark style="background-color:green;">**recommend using
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    GLM-4.6 is the latest reasoning model from **Z.ai**, achieving SOTA performance on coding and agent benchmarks while offering improved conversational chats. The full 355B parameter model requires **400GB** of disk space, while the Unsloth D
  • Network accessline 4506
    You asked for benchmarks on our quants, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing many full-precision SOTA LLMs. [Read more.](https://docs.unsloth.ai/new/unsloth-dynamic-gg
  • Network accessline 4509
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and Aider performance, meaning you can run & fine-tune quantized GLM LLMs with minimal accuracy loss.
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    {% stepper %} {% step %} Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp).
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    Install `ollama` if you haven't already! To run more variants of the model, [see here](https://docs.unsloth.ai/deepseek-v3.1-how-to-run-locally#run-in-llama.cpp).
  • Network accessline 4566
    {% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus
  • Network accessline 4568
    Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 4632
    Unsloth Docker image: [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth)
  • Network accessline 4643
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\
  • Network accessline 4644
    Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation):
  • Dangerous shell operationline 4647
    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
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    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For Blackwell and 50-series GPUs, use this same image - no separate one needed.
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    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth.
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    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
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    One of the key parts of creating a dataset is your [chat template](https://docs.unsloth.ai/basics/chat-templates) and how you are going to design it. Tokenization is also important as it breaks text into tokens, which can be words, sub-word
  • Network accessline 4759
    * Chat-based dialogues (Q\&A, learn a new language, customer support, conversations). * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.
  • Network accessline 4760
    * Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb), summarization, generation tasks).
  • Network accessline 4789
    For [**continued pretraining**](https://docs.unsloth.ai/basics/continued-pretraining), we use raw text format without specific structure:
  • Network accessline 4793
    If we are adapting a model to a new task, and intend for the model to output text in a single turn based on a specific set of instructions, we can use **Instruction** format in [Alpaca style](https://docs.unsloth.ai/basics/tutorial-how-to-f
  • Network accessline 4828
    <mark style="color:green;">**A:**</mark>  If your dataset is already formatted in the Alpaca format, then follow the formatting steps as shown in the Llama3.1 [notebook ](https://colab.research.google.com/github/unslothai/notebooks/blob/mai
  • Network accessline 4836
    <mark style="color:green;">**A:**</mark>  The `chat_template` attribute when a model is first uploaded by the original model owners sometimes contains errors and may take time to be updated. In contrast, at Unsloth, we thoroughly check and
  • Network accessline 4840
    <mark style="color:green;">**A:**</mark>  Submit a feature request on the unsloth github issues [forum](https://github.com/unslothai/unsloth). As a temporary workaround, you could also use the tokenizer's own apply\_chat\_template function
  • Network accessline 4846
    * Produce entirely new data - either from scratch or from your existing dataset * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting)
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    * Diversify your dataset so your model does not [overfit](https://docs.unsloth.ai/get-started/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting) and become too specific
  • Network accessline 4852
    We collaborated with Meta to launch a free notebook for creating Synthetic Datasets automatically using local models like Llama 3.2. [Access the notebook here.](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_
  • Network accessline 4889
    If you want to fine-tune a model that already has reasoning capabilities like the distilled versions of DeepSeek-R1 (e.g. DeepSeek-R1-Distill-Llama-8B), you will need to still follow question/task and answer pairs however, for your answer y
  • Network accessline 4891
    For a model that does not have reasoning and you want to train it so that it later encompasses reasoning capabilities, you will need to utilize a standard dataset but this time without reasoning in its answers. This is training process is k
  • Network accessline 4897
    * Standardize the format of all datasets, combine them into a single dataset, and fine-tune on this unified dataset. * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebo
  • Network accessline 4898
    * Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebook to fine-tune on multiple datasets directly.
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    You can access the GPT4 version of the Alpaca dataset [here](https://huggingface.co/datasets/vicgalle/alpaca-gpt4.). Below shows some examples of the dataset:
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    The dataset for fine-tuning a vision or multimodal model also includes image inputs. For example, the [Llama 3.2 Vision Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scroll
  • Network accessline 4990
    We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and
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    | Image
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    [Credential-related source omitted]
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    For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX).
  • Network accessline 5082
    * **Operating System**: Works on Linux and Windows. * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.u
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    * Supports NVIDIA GPUs since 2018+ including [Blackwell RTX 50](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [**DGX Spark**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-sp
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    Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20 & 50, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
  • Network accessline 5085
    * The official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker Hub.
  • Network accessline 5086
    * Unsloth works on [AMD](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) and [Intel](https://github.com/unslothai/unsloth/pull/2621) GPUs! Apple/Silicon/MLX is in the works.
  • Network accessline 5101
    **For context length benchmarks, see** [**here**](https://docs.unsloth.ai/basics/unsloth-benchmarks#context-length-benchmarks)**.**
  • Network accessline 5134
    <table><thead><tr><th width="212.9000244140625">Argument</th><th>Example and use-case</th></tr></thead><tbody><tr><td><strong><code>--gpu-memory-utilization</code></strong></td><td>Default 0.9. How much VRAM usage vLLM can use. Reduce if go
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    curl -X POST http://localhost:8000/v1/load_lora_adapter \
  • Network accessline 5204
    Qwen released QwQ-32B - a reasoning model with performance comparable to DeepSeek-R1 on many [benchmarks](https://qwenlm.github.io/blog/qwq-32b/). However, people have been experiencing **infinite generations**, **many repetitions**, \<thin
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    | [GGUF](https://huggingface.co/unsloth/QwQ-32B-GGUF) | [Dynamic 4-bit](https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit) | [BnB 4-bit](https://huggingface.co/unsloth/QwQ-32B-bnb-4bit) | [16-bit](https://huggingface.co/unsloth/QwQ-32
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    According to [Qwen](https://huggingface.co/Qwen/QwQ-32B), these are the recommended settings for inference:
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    Courtesy to [@krist486](https://x.com/krist486/status/1897885598196654180) for bringing llama.cpp sampling directions to my attention.
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    We investigated usage of `dry penalty`  as suggested in <https://github.com/ggml-org/llama.cpp/blob/master/examples/main/README.md> using a value of 0.8, but we actually found this to **rather cause syntax issues especially for coding**. If
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/QwQ-32B-GGUF>
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    Qwen3-VL has vision, video and OCR capabilities as well as 256K context (can be extended to 1M).\ \ [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcem
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    [Unsloth](https://github.com/unslothai/unsloth) supports **Qwen3-VL fine-tuning and** [**RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl). Train Qwen3-VL (8B) for free with our [notebooks](#fine-tuning-qwen3-vl).
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    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF">2B-Instruct</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL-2B-Thinking-GGUF">2B-Thinking</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-VL
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    Qwen3-VL also used the below settings for their benchmarking numbers, as mentioned [on GitHub](https://github.com/QwenLM/Qwen3-VL/tree/main?tab=readme-ov-file#generation-hyperparameters).
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    2. **Let's first get an image!** You can also upload images as well. We shall use <https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/unsloth%20made%20with%20love.png>, which is just our mini logo showing how finetun
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    4. Let's get the 2nd image at <https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg>
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    [Credential-related source omitted]
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    We uploaded dynamic 4-bit quants to: <https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit>
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    Since vLLM 0.7.3 (2025 February 20th) <https://github.com/vllm-project/vllm/releases/tag/v0.7.3>, vLLM now supports loading Unsloth dynamic 4bit quants!
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    All our GGUFs are at <https://huggingface.co/unsloth/QwQ-32B-GGUF>!
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    According to the Min\_p paper <https://arxiv.org/pdf/2407.01082>, for more creative and diverse outputs, and if you still see repetitions, try disabling top\_p and top\_k!
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    {% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %}
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    {% embed url="<https://www.youtube.com/watch?t=3289s&v=bbFEYPx9Hpo>" %}
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    {% embed url="<https://www.youtube.com/watch?v=juOh1afy-IE>" %}
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    {% embed url="<https://www.youtube.com/watch?v=oF0_eMhzRaQ>" %}
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    You can then run the merged QAT lower precision model in vLLM, Unsloth and other systems for inference! These are all in the [Qwen3-4B QAT Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\
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    <table data-full-width="true"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Accuracy</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.58bit</td><td>UD-IQ1_S</td><td><strong>131GB</strong></td><td>Fair</td><td>
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    5. To test our Flappy Bird example as mentioned in our blog post here: <https://unsloth.ai/blog/deepseekr1-dynamic>, we can produce the 2nd example like below using our 1.58bit dynamic quant:
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    [Credential-related source omitted]
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    <table><thead><tr><th width="249">Dynamic GGUFs</th><th>Dynamic 4-bit + FP8</th><th>16-bit Instruct</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/granite-4.0-h-350m-GGUF">H-350M</a></li><li><a href="https:/
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    You can also view our [Granite-4.0 collection](https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d) for all uploads including Dynamic Float8 quants etc.
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    * **LoRA:** 16-bit fine-tuning. It's slightly faster and slightly more accurate, but consumes significantly more VRAM (4× more than QLoRA). Recommended for 16-bit environments and scenarios where maximum accuracy is required. * **QLoRA:** 4
  • Network accessline 6103
    :sloth: *70B LLaMA fits in <48GB VRAM with QLoRA in Unsloth -* [*more details here*](https://unsloth.ai/blog/llama3-3)*.*
  • Network accessline 6147
    [Read our blog post](https://unsloth.ai/blog/gradient) for more details.
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    A regularization technique that helps [prevent overfitting](#overfitting-poor-generalization-too-specialized) by randomly setting a fraction of the LoRA activations to zero during each training step. [Recent research suggests](https://arxiv
  • Network accessline 6174
    Options are `True`, `False`, and `"unsloth"`. \    🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](
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    🦥 *We recommend* `"unsloth"` *as it reduces memory usage by an extra 30% and supports extremely long context fine-tunes. You can read more on* [*our blog post about long context training*](https://unsloth.ai/blog/long-context)*.*\\
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    An advanced feature that implements [**Rank-Stabilized LoRA**](https://arxiv.org/abs/2312.03732). If set to `True`, the effective scaling becomes `lora_alpha / sqrt(r)` instead of the standard `lora_alpha / r`. This can sometimes improve st
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    An advanced technique, as proposed in [**LoftQ**](https://arxiv.org/abs/2310.08659), initializes LoRA matrices with the top 'r' singular vectors from the pretrained weights. This can improve accuracy but may cause a significant memory spike
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    When validating that **LoRA** adapter weights have been updated after fine-tuning, avoid using **np.allclose()** for comparison. This method can miss subtle but meaningful changes, particularly in **LoRA A**, which is initialized with small
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    According to the [rsLoRA (rank stabilized lora) paper](https://arxiv.org/abs/2312.03732), we should instead scale alpha by the sqrt of the rank. Other options exist, but theoretically this is the optimum. The left plot shows other ranks and
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    According to empirical experiments and research papers like the original [QLoRA paper](https://arxiv.org/pdf/2305.14314), it's best to apply LoRA to both attention and MLP layers.
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    [Credential-related source omitted]
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    The [QLoRA paper](https://arxiv.org/pdf/2305.14314) shows that masking out inputs and **training only on completions** (outputs or assistant messages) can further **increase accuracy** by a few percentage points (*1%*). Below demonstrates h
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    The QLoRA paper states that **training on completions only** increases accuracy by quite a bit, especially for multi-turn conversational finetunes! We do this in our [conversational notebooks here](https://colab.research.google.com/github/u
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    ***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!*
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    {% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    This article covers everything (from beginner to advanced) you need to know about GRPO, Reinforcement Learning (RL) and reward functions, along with tips, and the basics of using GRPO with [Unsloth](https://github.com/unslothai/unsloth). If
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    {% column %} OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**<
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    OpenAI popularized the concept of [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) (Reinforcement Learning from Human Feedback), where we train an <mark style="background-color:red;">**"agent"**</mark> to pro
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    {% column %} In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agen
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    In order to do RLHF, [<mark style="background-color:red;">**PPO**</mark>](https://en.wikipedia.org/wiki/Proximal_policy_optimization) (Proximal policy optimization) was developed. The <mark style="background-color:blue;">**agent**</mark> is
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    The formula for PPO looks quite complicated because it was designed to be stable. Visit our [AI Engineer talk](https://docs.unsloth.ai/ai-engineers-2025) we gave in 2025 about RL for more in depth maths derivations about PPO.
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    {% column %} DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are:
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    DeepSeek developed [<mark style="background-color:red;">**GRPO**</mark>](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. The key differences to PPO are:
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    2. Code output can be verified as having executed correctly or not. 3. Designing verifiable reward functions can be tough, and so most examples are math or code. 4. Use-cases for GRPO isn’t just for code or math—its reasoning process can en
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    4. Use-cases for GRPO isn’t just for code or math—its reasoning process can enhance tasks like email automation, database retrieval, law, and medicine, greatly improving accuracy based on your dataset and reward function - the trick is to d
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    * With 15GB VRAM, Unsloth allows you to transform any model up to 17B parameters like Llama 3.1 (8B), Phi-4 (14B), Mistral (7B) or Qwen2.5 (7B) into a reasoning model * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.un
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    * **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!**
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    {% content-ref url="reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo" %} [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train
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    [tutorial-train-your-own-reasoning-model-with-grpo](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo)
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    | [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **GSPO -** new | [**Qwen3-VL-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_
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    | -------------------------------------------------------------------------------------------------------------------------------------- | -----------------------------------------------------------------------------------------------------
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    | [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced         | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/mai
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    | [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)                     | [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)
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    | [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)          | [Llama 3.1 (8B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\
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    {% hint style="success" %} **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in
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    **NEW!** We now support [**GSPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning) and most other new GRPO techniques. You can play with the following arguments in GRPOConfig to enable:
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    {% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM,
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    [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#new-saving-to-gguf-vllm-after-gpt-oss-training)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc.
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    We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t
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    > [**Fine-tune**](#fine-tuning-gpt-oss-with-unsloth) **gpt-oss-20b for free with our** [**Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb)
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    Trained with [RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), **gpt-oss-120b** rivals o4-mini and **gpt-oss-20b** rivals o3-mini. Both excel at function calling and CoT reasoning, surpassing o1 and GPT-4o.
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    * 20B: [gpt-oss-**20B**](https://huggingface.co/unsloth/gpt-oss-20b-GGUF)
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    * 120B: [gpt-oss-**120B**](https://huggingface.co/unsloth/gpt-oss-120b-GGUF)
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    OpenAI released a standalone parsing and tokenization library called [Harmony](https://github.com/openai/harmony) which allows one to tokenize conversations to OpenAI's preferred format for gpt-oss. The official OpenAI [cookbook article](ht
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    We made a [MXFP4 inference notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) as well in Tesla T4 Colab!
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    {% hint style="info" %} [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case,
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    [Software emulation](https://triton-lang.org/main/python-api/generated/triton.language.dot_scaled.html) enables targeting hardware architectures without native microscaling operation support. Right now for such case, microscaled lhs/rhs are
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    To achieve inference speeds of 6+ tokens per second for our Dynamic 4-bit quant, have at least **14GB of unified memory** (combined VRAM and RAM) or **14GB of system RAM** alone. As a rule of thumb, your available memory should match or exc
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    > **You can run gpt-oss-20b for free with our** [**Google Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb)
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    **Magistral-Small-2509** is a reasoning LLM developed by Mistral AI. It excels at coding and mathematics and supports multiple languages.  Magistral supports a 128k token context window and was finetuned from [**Mistral-Small-3.2**](https:/
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    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Mistral LLMs with minimal accuracy loss.
  • Network accessline 6839
    <table><thead><tr><th width="255.64999389648438">Dynamic 2.0 GGUF (to run)</th><th width="305.25">Dynamic 4-bit (to finetune/deploy)</th><th>Dynamic Float8</th></tr></thead><tbody><tr><td><ul><li><a href="https://huggingface.co/unsloth/Magi
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    2. Run the model with our dynamic quant. We did not set the context length automatically, so it will just use Ollama's default set context length.\    Note you can call `ollama serve &`in another terminal if it fails! We include all suggest
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    Note you can call `ollama serve &`in another terminal if it fails! We include all suggested parameters (temperature etc) in `params` in our Hugging Face upload! 3. Also Magistral supports 40K context lengths, so best to enable [**KV cache q
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    3. Also Magistral supports 40K context lengths, so best to enable [**KV cache quantization**](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-set-the-quantization-type-for-the-kv-cache). We use 8bit quantization which saves
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    4. **Ollama also sets the default context length to 4096**, as [mentioned here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size). Use `OLLAMA_CONTEXT_LENGTH=8192` to change it to 8192. Magist
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 6942
    ## From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html
  • Network accessline 6944
    **URL:** llms-txt#from-https://mlabonne.github.io/blog/posts/quantize_llama_2_models_using_ggml.html
  • Network accessline 6981
    bash apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggerganov/llama.cpp
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    bash apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggerganov/llama.cpp
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    Or follow the steps at <https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model> using the model name "merged\_model" to merge to GGUF.
  • Network accessline 7034
    * It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no
  • Network accessline 7035
    * <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark>   * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/
  • Network accessline 7036
    * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)
  • Network accessline 7037
    * Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb)
  • Network accessline 7038
    * Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 7039
    * Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
  • Network accessline 7040
    * Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
  • Network accessline 7041
    * **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks)
  • Network accessline 7074
    Microsoft's new Phi-4 reasoning models are now supported in Unsloth. The 'plus' variant performs on par with OpenAI's o1-mini, o3-mini and Sonnet 3.7. The 'plus' and standard reasoning models are 14B parameters while the 'mini' has 4B param
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    All Phi-4 reasoning uploads use our [Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology.
  • Network accessline 7080
    | Dynamic 2.0 GGUF (to run)
  • Network accessline 7082
    | <ul><li><a href="https://huggingface.co/unsloth/Phi-4-reasoning-plus-GGUF/">Reasoning-plus</a> (14B)</li></ul><ul><li><a href="https://huggingface.co/unsloth/Phi-4-reasoning-GGUF">Reasoning</a> (14B)</li></ul><ul><li><a href="https://hugg
  • Network accessline 7121
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    Fine-tuning vision models enables model to excel at certain tasks normal LLMs won't be as good as such as object/movement detection. **You can also train** [**VLMs with RL**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl)
  • Network accessline 7163
    * **NEW: Qwen3-VL (8B) Vision:** [**Notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb)
  • Network accessline 7164
    * **Gemma 3 (4B) Vision:** [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb)
  • Network accessline 7165
    * **Llama 3.2 Vision** fine-tuning for radiography: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb)\
  • Network accessline 7166
    How can we assist medical professionals in analyzing Xrays, CT Scans & ultrasounds faster. * **Qwen2.5 VL** fine-tuning for converting handwriting to LaTeX: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/n
  • Network accessline 7167
    * **Qwen2.5 VL** fine-tuning for converting handwriting to LaTeX: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb)\
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    This allows complex math formulas to be easily transcribed as LaTeX without manually writing it. * **Pixtral 12B 2409** vision fine-tuning for general Q\&A: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/n
  • Network accessline 7169
    * **Pixtral 12B 2409** vision fine-tuning for general Q\&A: [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb)\
  • Network accessline 7180
    The dataset for fine-tuning a vision or multimodal model is similar to standard question & answer pair [datasets ](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide), but this time, they also includes image inputs. F
  • Network accessline 7182
    We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Funsloth%2FRadiology_mini). The dataset includes X-rays, CT scans and
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    | Image
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    [Credential-related source omitted]
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    For more details, view our dataset section in the [notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb#scrollTo=vITh0KVJ10qX).
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    [Reward Functions/Verifiers](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#reward-functions-verifier) lets us know if the model is doing well or not according to the dataset you have provided. Each generation run wi
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    You can input your generations into an LLM like ChatGPT 4o or Llama 3.1 (8B) and design a reward function and verifier to evaluate it. For example, feed your generations into a LLM of your choice and set a rule: "If the answer sounds too ro
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    [Credential-related source omitted]
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    We have pre-selected hyperparameters for the most optimal results however you could change them. Read all about [parameters here](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide). For **advanced GRPO**
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    [Credential-related source omitted]
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    LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs.
  • Network accessline 7366
    RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo
  • Network accessline 7368
    | [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo
  • Network accessline 7392
    Load the 20B model in 4‑bit QLoRA for memory efficiency, then wrap it with a LoRA adapter. You can also train it in 16-bit LoRA but it will use 4x more memory. For more settings view our [configuration guide](https://docs.unsloth.ai/get-sta
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    !uv pip install -qqq \     "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
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    "torch>=2.8.0" "triton>=3.4.0" {get_numpy} torchvision bitsandbytes "transformers==4.56.2" \     "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \     "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
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    "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
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    "unsloth[base] @ git+https://github.com/unslothai/unsloth" \
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    git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels
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    DeepSeek’s V3.1 and **Terminus** update introduces hybrid reasoning inference, combining 'think' and 'non-think' into one model. The full 671B parameter model requires 715GB of disk space. The quantized dynamic 2-bit version uses 245GB (-75
  • Network accessline 7676
    {% hint style="success" %} **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\
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    **NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\
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    [**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing
  • Network accessline 7684
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized DeepSeek LLMs with minimal accuracy loss.
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    1. DeepSeek V3.1 is a hybrid reasoning model, meaning you can change the chat template to enable reasoning. The chat template introduced `thinking = True` , but other models use `enable_thinking = True` . We added the option to use `enable_
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    2. llama.cpp's jinja renderer via [minja](https://github.com/google/minja) does not allow the use of extra arguments in the `.split()` command, so using `.split(text, 1)` works in Python, but not in minja. We had to change this to make llam
  • Network accessline 7716
    According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3.1), these are the recommended settings for V3.1 inference:
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    {% step %} Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant.
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    Open WebUI also made a [step-by-step tutorial](https://docs.openwebui.com/tutorials/integrations/deepseekr1-dynamic/) on how to run R1 and for V3.1, you will just need to replace R1 with the new V3.1 quant.
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    {% stepper %} {% step %} Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or jus
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    Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
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    url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
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    year         = {2025},   publisher    = {Hugging Face},   howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}}
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    publisher    = {Hugging Face},   howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} }
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    howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}}
  • Network accessline 7971
    year         = {2025},   publisher    = {Github},   howpublished = {\url{https://github.com/unslothai/unsloth}}
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    publisher    = {Github},   howpublished = {\url{https://github.com/unslothai/unsloth}} }
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    howpublished = {\url{https://github.com/unslothai/unsloth}}
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    * For more detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).&#x20;
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    * Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
  • Network accessline 8031
    Tested on H100 and [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) GPUs. We tested using the Alpaca Dataset, a batch size of 2, gradient accumulation steps of 4, rank = 32, and applied Q
  • Network accessline 8037
    {% hint style="info" %} The more data you have, the less VRAM Unsloth uses due to our [gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm + Apple's CCE algorithm!
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    The more data you have, the less VRAM Unsloth uses due to our [gradient checkpointing](https://unsloth.ai/blog/long-context) algorithm + Apple's CCE algorithm!
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    As shown at [OpenAI DevDay](https://x.com/UnslothAI/status/1976284209842118714), gpt-oss-20b was trained with RL and Unsloth on DGX Spark to auto-win 2048. You can train using Unsloth in a Docker container or virtual environment on DGX Spar
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    First, build the Docker image using the DGX Spark Dockerfile which can be [found here](https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark). You can also run the below in a Terminal in the DGX Spark:
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    sudo apt update && sudo apt install -y wget
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    sudo apt update && sudo apt install -y wget wget -O Dockerfile "https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark"
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    wget -O Dockerfile "https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark"
  • Network accessline 8135
    You can fine-tune DeepSeek-OCR to enhance its vision or language performance. In our Unsloth [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb), we demonstrat
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    > **Our model upload that enables fine-tuning + more inference support:** [**DeepSeek-OCR**](https://huggingface.co/unsloth/DeepSeek-OCR)
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    {% hint style="success" %} [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF
  • Network accessline 8185
    [**Aug 28 update**](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support)**:** You can now export/save your QLoRA fine-tuned gpt-oss model to llama.cpp, vLLM, HF etc.
  • Network accessline 8187
    We also introduced [Unsloth Flex Attention](https://docs.unsloth.ai/models/long-context-gpt-oss-training#introducing-unsloth-flex-attention-support) which enables **>8× longer context lengths**, **>50% less VRAM usage** and **>1.5× faster t
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    > **Quickstart:** Fine-tune gpt-oss-20b for free with our: [Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb)
  • Network accessline 8201
    This section covers fine-tuning gpt-oss using our Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). You can also save and use the gpt-oss notebook into your favorite code editor and follow our [local gpt-oss g
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    We’ll load **`gpt-oss-20b`**  using Unsloth's [linearized version](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#making-efficient-gpt-oss-fine-tuning-work) (as no other version will work).&#x20;
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    Now it's time to adjust your training hyperparameters. For a deeper dive into how, when, and what to tune, check out our [detailed hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
  • Network accessline 8237
    {% hint style="info" %} To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.
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    To avoid [overfitting](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide#avoiding-overfitting-and-underfitting), monitor your training loss and avoid setting these values too high.&#x20;
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    For this example, we will use the [`HuggingFaceH4/Multilingual-Thinking`](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking). This dataset contains chain-of-thought reasoning examples derived from user questions translated
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    One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` ,
  • Network accessline 8269
    {% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 8270
    🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 8273
    Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide).
  • Network accessline 8276
    We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 8322
    [Credential-related source omitted]
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    {% hint style="info" %} We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use.
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    We recommend downloading or incorporating elements from our Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) into your local setup for easier use.
  • Network accessline 8339
    Ensure your device is [Unsloth compatible](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and you can read our detailed [installation guide](https://docs.unsloth.ai/get-started/install-and-update).
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    [Credential-related source omitted]
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    One unique feature of gpt-oss is its use of the [**OpenAI Harmony format**](https://github.com/openai/harmony)**,** which supports structured conversations, reasoning output, and tool calling. This format includes tags such as `<|start|>` ,
  • Network accessline 8368
    {% hint style="info" %} 🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 8369
    🦥 Unsloth fixes the chat template to ensure it is correct. See this [tweet](https://x.com/danielhanchen/status/1953901104150065544) for technical details on our template fix.
  • Network accessline 8372
    Feel free to adapt the prompt and structure to suit your own dataset or use-case. For more guidance, refer to our [dataset guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide).
  • Network accessline 8379
    We've pre-selected training hyperparameters for optimal results. However, you can modify them based on your specific use case. Refer to our [hyperparameters guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparamet
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    This can zero out all `completion_mask` entries when many completions are truncated, making `n_mask_per_reward = 0` and causing KL to become NaN. [See](https://github.com/unslothai/unsloth-zoo/blob/e705f7cb50aa3470a0b6e36052c61b7486a39133/u
  • Network accessline 8632
    In our GitHub, we have a list of every chat template Unsloth uses including for Llama, Mistral, Phi-4 etc. So if you need any pointers on the formatting or use case, you can view them here: [github.com/unslothai/unsloth/blob/main/unsloth/ch
  • Network accessline 8636
    * [Conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
  • Network accessline 8637
    * [ChatML](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
  • Network accessline 8638
    * [Ollama](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)
  • Network accessline 8639
    * [Text Classification](https://github.com/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) by Timotheeee
  • Network accessline 8640
    * [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) by Flail
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    Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab  notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin
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    We’re excited to share that Unsloth Dynamic GGUFs shows how it's possible to quantize LLMs like [DeepSeek-V3.1](https://docs.unsloth.ai/models/deepseek-v3.1-how-to-run-locally) (671B) down to just **1-bit** or **3-bit**, and still be able t
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    Previously, [we demonstrated](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) how Unsloth Dynamic GGUFs outperform other quantization methods on 5-shot MMLU and KL Divergence. Now, we’re showcasing their performance on independent
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    **Why the** [**Aider Polyglot**](https://aider.chat/docs/leaderboards/) **benchmark?** Aider is one of the most comprehensive measures of how well LLMs can write, code, follow instructions, and apply changes without human intervention, maki
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    {% hint style="success" %} The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalL
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    The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3
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    In Nov 2024, our [4-bit Dynamic](https://unsloth.ai/blog/dynamic-4bit) Quants showcased how you could largely restore QLoRA fine-tuning & model accuracy by just <mark style="background-color:green;">**selectively quantizing layers**</mark>.
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    Our Dynamic GGUFs are even more effective when paired with our [imatrix calibration dataset](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs#whats-new-in-dynamic-v2.0), designed for chat and coding performance. All of this enabled
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    We also showed dynamic benchmarks in <https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs> for Gemma 3 and Llama 4 Scout, showing how effective our methodology is:
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    Benchmark experiments were mainly conducted by [David Sluys](https://www.linkedin.com/in/david-sluys-231348208/) (neolithic5452 on [Aider Discord](https://discord.com/channels/1131200896827654144/1408293692074360914)), a trusted community c
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    See [here](https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF?chat_template=default\&format=true) for our fixed chat template or [here](https://huggingface.co/unsloth/DeepSeek-V3.1/raw/main/chat_template.jinja) for a raw jinja file.
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    Head over to our [DeepSeek V3.1 guide](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-how-to-run-locally/deepseek-r1-dynamic-1.58-bit) or to quickly get the dynamic 2bit version, do:
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    * <U+200B>**DeepSeek-R1-Distill-Llama-8B** is a fine-tuned version of Llama-3.1-8B. DeepSeek utilized data generated by DeepSeek-R1, to fine-tune Llama-3.1-8B. This process, known as distillation (a subcategory of fine-tuning), injects the data in
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    With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune for free on Colab, Kaggle, or locally with just 3GB VRAM by using our [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). By fine-tuning a pre-trained m
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    You may have heard that fine-tuning does not make a model learn new knowledge or RAG performs better than fine-tuning. That is **false**. Read more FAQ + misconceptions [here](https://docs.unsloth.ai/beginner-start-here/faq-+-is-fine-tuning
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    {% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    We recommend starting with **Instruct models**, as they allow direct fine-tuning using conversational chat templates (ChatML, ShareGPT etc.) and require less data compared to **Base models** (which uses Alpaca, Vicuna etc). Learn more about
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    * Model names ending in **`unsloth-bnb-4bit`** indicate they are [**Unsloth dynamic 4-bit**](https://unsloth.ai/blog/dynamic-4bit) **quants**. These models consume slightly more VRAM than standard BitsAndBytes 4-bit models but offer signifi
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    We recommend starting with QLoRA, as it is one of the most accessible and effective methods for training models. Our [dynamic 4-bit](https://unsloth.ai/blog/dynamic-4bit) quants, the accuracy loss for QLoRA compared to LoRA is now largely r
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    You can also do [Text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [reasoning (GRPO)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [vision](https://docs.unsloth.ai/basics/vision-f
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    {% content-ref url="fine-tuning-llms-guide/what-model-should-i-use" %} [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use)
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    [what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use)
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    * You will need to create a dataset usually with 2 columns - question and answer. The quality and amount will largely reflect the end result of your fine-tune so it's imperative to get this part right. * You can [synthetically generate data
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    * You can [synthetically generate data](https://docs.unsloth.ai/get-started/datasets-guide#synthetic-data-generation) and structure your dataset (into QA pairs) using ChatGPT or local LLMs.
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    * You can also use our new Synthetic Dataset notebook which automatically parses documents (PDFs, videos etc.), generates QA pairs and auto cleans data using local models like Llama 3.2. [Access the notebook here.](https://colab.research.go
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    {% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
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    [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
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    For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which
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    Learn how to choose the right [hyperparameters](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) using best practices from research and real-world experiments - and understand how each one affects your
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    {% content-ref url="fine-tuning-llms-guide/lora-hyperparameters-guide" %} [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    [lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide)
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    We would recommend beginners to utilise our pre-made [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) first as it's the easiest way to get started with guided steps. However, if installing locally is a must, you can instal
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    {% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
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    [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
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    {% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
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    For testing, you can also  take 20% of your training data and use that for testing. If you already used all of the training data, then you have to manually evaluate it. You can also use automatic eval tools like EleutherAI’s [lm-evaluation-
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    {% content-ref url="../basics/running-and-saving-models" %} [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models)
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    [running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models)
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    We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingfac
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    To learn more about fine-tuning tips and tricks, head over to our blogs which provide tremendous and educational value: <https://unsloth.ai/blog/>
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    If you need any help on fine-tuning, you can also join our Discord server [here](https://discord.gg/unsloth) or [Reddit r/unsloth](https://www.reddit.com/r/unsloth/). Thanks for reading and hopefully this was helpful!
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    # Fix up addCriterion issues         # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks
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    # See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks
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    [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide "mention")
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    ***Authors:** A huge thank you to* [*Keith*](https://www.linkedin.com/in/keith-truongcao-7bb84a23b/) *and* [*Datta*](https://www.linkedin.com/in/datta0/) *for contributing to this article!*
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    This was [reported](https://github.com/QwenLM/Qwen2.5-VL/issues/759) as well in Qwen2.5-VL-7B-Instruct output unexpected results "addCriterion". In fact we see this as well! We tried both non Unsloth, bfloat16 and float16 machines and other
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    This update in addition adds GSPO ([Group Sequence Policy Optimization](https://arxiv.org/abs/2507.18071)) which is a variant of GRPO made by the Qwen team at Alibaba. They noticed that GRPO implicitly results in importance weights for each
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    See our guide below for the complete process on how to save to [Ollama](https://github.com/ollama/ollama):
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    {% content-ref url="../../get-started/fine-tuning-llms-guide/tutorial-how-to-finetune-llama-3-and-use-in-ollama" %} [tutorial-how-to-finetune-llama-3-and-use-in-ollama](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/tutorial-how
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    [tutorial-how-to-finetune-llama-3-and-use-in-ollama](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/tutorial-how-to-finetune-llama-3-and-use-in-ollama)
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    You can save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via: <https://huggingface.c
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    Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf>
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    * It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses! * <mark style="background-color:green;">**Use our conversational no
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    * <mark style="background-color:green;">**Use our conversational notebooks to force the chat template - this will fix most issues.**</mark>   * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/
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    * Qwen-3 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb)
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    * Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb)
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    * Llama-3.2 3B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
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    * Phi-4 14B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
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    * Mistral v0.3 7B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
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    * **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks)
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    {% hint style="success" %} [**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF s
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    [**Sept 10, 2025 update:**](https://docs.unsloth.ai/new/unsloth-dynamic-ggufs-on-aider-polyglot) You asked for tougher benchmarks, so we’re showcasing Aider Polyglot results! Our Dynamic 3-bit DeepSeek V3.1 GGUF scores **75.6%**, surpassing
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    The **key advantage** of using the Unsloth package and models is our active role in ***fixing critical bugs*** in major models. We've collaborated directly with teams behind [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3
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    | **Qwen3:** [0.6B](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF) • [1.7B](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF) • [4B](https://huggingface.co/unsloth/Qwen3-4B-GGUF) • [8B](https://huggingface.co/unsloth/Qwen3-8B-GGUF) • [14B](h
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    | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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    | **DeepSeek:** [R1-0528](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/deepseek-r1-0528-how-to-run-locally#model-uploads) • [V3-0324](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF-UD) • [R1-Distill-Llama](ht
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    | **Gemma 3:** [4B](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) • [12B](https://huggingface.co/unsloth/gemma-3-12b-it-GGUF) • [27B](https://huggingface.co/unsloth/gemma-3-27b-it-GGUF) • [QAT](https://huggingface.co/unsloth/gemma-3-12
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    [Accuracy is Not All You Need](https://arxiv.org/pdf/2407.09141) showcases how pruning layers, even by selecting unnecessary ones still yields vast differences in terms of "flips". A "flip" is defined as answers changing from incorrect to c
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    Most frameworks report perplexity and KL Divergence using a test set of Wikipedia articles. However, we noticed using the calibration dataset which is also Wikipedia related causes quants to overfit, and attain lower perplexity scores. We u
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    * Llama 3.1 (8B) Instruct has a MMLU 5 shot accuracy of 67.8% using a naive MMLU implementation. We find however Llama **tokenizes "A" and "\_A" (A with a space in front) as different token ids**. If we consider both spaced and non spaced t
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    * Interestingly Llama 3 as per Eleuther AI's [LLM Harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/llama3/instruct/mmlu/_continuation_template_yaml) also appends <mark style="background-color:purple;">**"
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    * There are many other subtle issues, and so to benchmark everything in a controlled environment, we designed our own MMLU implementation from scratch by investigating [github.com/hendrycks/test](https://github.com/hendrycks/test) directly,
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    1. Q4\_0 GGUF - Quantizes all layers to Q4\_0 via the formula `w = q * block_scale` with each block having 32 weights. See [llama.cpp wiki ](https://github.com/ggml-org/llama.cpp/wiki/Tensor-Encoding-Schemes)for more details.
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    2. int4 version - presumably [TorchAO int4 style](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md)?
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    * Llama 4 Scout changed the RoPE Scaling configuration in their official repo. We helped resolve issues in llama.cpp to enable this [change here](https://github.com/ggml-org/llama.cpp/pull/12889)
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    * Llama 4's QK Norm's epsilon for both Scout and Maverick should be from the config file - this means using 1e-05 and not 1e-06. We helped resolve these in [llama.cpp](https://github.com/ggml-org/llama.cpp/pull/12889) and [transformers](htt
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    * The Llama 4 team and vLLM also independently fixed an issue with QK Norm being shared across all heads (should not be so) [here](https://github.com/vllm-project/vllm/pull/16311). MMLU Pro increased from 68.58% to 71.53% accuracy.
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    * [Wolfram Ravenwolf](https://x.com/WolframRvnwlf/status/1909735579564331016) showcased how our GGUFs via llama.cpp attain much higher accuracy than third party inference providers - this was most likely a combination of the issues explaine
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    A huge thank you to Rohan Pandey for his [Flex Attention implementation](https://x.com/khoomeik/status/1955693558914310608), which directly inspired the development of Unsloth's Flex Attention implementation.
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    OpenAI leveraged **Attention Sinks** from the Efficient Streaming Language Models with Attention Sinks [paper](https://arxiv.org/abs/2309.17453) which shows that you can use a small sliding window, except you must add a global attention on
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    The paper also shows that the [Attention Is Off By One method](https://www.evanmiller.org/attention-is-off-by-one.html) does partially work, except one must also add a few extra sink tokens to get lower perplexities. **The paper shows that
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    Flex Attention <https://pytorch.org/blog/flexattention/> is extremely powerful as it provides the practitioner 2 customization routes for the attention mechanism - a **score modifier (f)** and a **masking function (M)**.
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    By using some visualization utilities from [Flex Attention's Github repo](https://github.com/meta-pytorch/attention-gym), we can visualize this. Assume the sequence length was 16, and a sliding window of 5. On the left is the last sink colu
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    **Interesting finding**: The official Flex Attention sliding window implementations considers the window size as the number of last tokens **PLUS ONE** as it includes the current token. The HuggingFace and GPT OSS implementations strictly o
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    We also confirmed through OpenAI's official GPT-OSS implementation on whether we attend to the last N or N+1 tokens here: <https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py>
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    To run inference on the merged model, you can use vLLM and Llama.cpp among others. OpenAI recommends these [inference settings](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/..#recommended-settings) for both models: `tempe
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    We [recently collaborated with Hugging Face](https://github.com/huggingface/transformers/pull/40197) to resolve inference issues by using OpenAI’s kernels and ensuring that `swiglu_limit = 7.0` is correctly applied during MXFP4 inference.
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    OpenAI's sink token implementation is [provided here](https://github.com/openai/gpt-oss/blob/main/gpt_oss/torch/model.py). We provide it below:
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    The HuggingFace transformers implementation is [provided here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_oss/modeling_gpt_oss.py). We also provide it below:
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    [Credential-related source omitted]
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    * Use `USER_PASSWORD` for sudo operations inside container
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    {% columns %} {% column %} With Unsloth and TorchAO’s QAT support, you can also fine-tune a model in Unsloth and seamlessly export it to [ExecuTorch](https://github.com/pytorch/executorch) (PyTorch’s solution for on-device inference) and de
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    With Unsloth and TorchAO’s QAT support, you can also fine-tune a model in Unsloth and seamlessly export it to [ExecuTorch](https://github.com/pytorch/executorch) (PyTorch’s solution for on-device inference) and deploy it directly on mobile.
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    [Credential-related source omitted]
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    Then **try QAT with our free** [**Qwen3 (4B) notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb)
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    DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models.
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    These instructions are for our pre-made Google Colab [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks). If you are installing Unsloth locally, you can also copy our notebooks inside your favorite code editor. We'll be using
  • Network accessline 9990
    | [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) **-** GSPO | [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_
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    | [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - Advanced     | [**DeepSeek-R1-0528-Qwen3-8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb
  • Network accessline 9997
    If you're using our Colab notebook, click **Runtime > Run all**. We'd highly recommend you checking out our [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) before getting started.
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    If installing locally, ensure you have the correct [requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) and use `pip install unsloth` on Linux or follow our [Windows install ](https://docs.unsloth.ai/
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    [Credential-related source omitted]
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    Before we get started, it is recommended to learn more about GRPO, reward functions and how they work. Read more about them including [tips & tricks](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#basics-tips)[ here]
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    We have pre-selected optimal settings for the best results for you already and you can change the model to whichever you want listed in our [supported models](https://docs.unsloth.ai/get-started/all-our-models). Would not recommend changing
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    {% hint style="success" %} For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    For **advanced GRPO** documentation on batching, generation and training parameters, [read our guide!](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation)
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    [Credential-related source omitted]
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    We have pre-selected OpenAI's [GSM8K](https://huggingface.co/datasets/openai/gsm8k) dataset which contains grade school math problems but you could change it to your own or any public one on Hugging Face. You can read more about [datasets h
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    {% hint style="success" %} **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507)
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    **NEW!** Qwen3 got an update in July 2025. Run & fine-tune the latest model: [**Qwen-2507**](https://docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune/qwen3-2507)
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    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run & fine-tune quantized Qwen LLMs with minimal accuracy loss.
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    [Unsloth](https://github.com/unslothai/unsloth) also now supports fine-tuning and [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) of Qwen3 and Qwen3 MOE models — 2x faster, with 70% less VR
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    | Dynamic 2.0 GGUF (to run)
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    | <ul><li><a href="https://huggingface.co/unsloth/Qwen3-0.6B-GGUF">0.6B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-1.7B-GGUF">1.7B</a></li><li><a href="https://huggingface.co/unsloth/Qwen3-4B-GGUF">4B</a></li><li><a href="htt
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    Qwen3 models come with built-in "thinking mode" to boost reasoning and improve response quality - similar to how [QwQ-32B](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/qwq-32b-how-to-run-effectively) worked. Instru
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    ## Go to https://docs.unsloth.ai for advanced tips like
  • Network accessline 10185
    **URL:** llms-txt#go-to-https://docs.unsloth.ai-for-advanced-tips-like
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    We're introducing GSPO which is a variant of [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/..#from-rlhf-ppo-to-grpo-and-rlvr) made by the Qwen team at Alibaba. They noticed the observation that when GRPO takes i
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    * Use our free GSPO notebooks for: [**gpt-oss-20b**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) and [**Qwen2.5-VL**](https://colab.research.google.com/github/unslothai/notebooks/blo
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    With [Unsloth](https://github.com/unslothai/unsloth), you can fine-tune TTS models 1.5x faster with 50% less memory than other implementations with Flash Attention 2. This support includes Sesame CSM, Orpheus, and models supported by transf
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    We've uploaded TTS models (original and quantized variants) to our [Hugging Face page](https://huggingface.co/collections/unsloth/text-to-speech-tts-models-68007ab12522e96be1e02155).
  • Network accessline 10276
    | [Sesame-CSM (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb) | [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipy
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    | [Spark-TTS (0.5B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb)   | [Llasa-TTS (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb)
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    At minimum, a TTS fine-tuning dataset consists of **audio clips and their corresponding transcripts** (text). Let’s use the [*Elise* dataset](https://huggingface.co/datasets/MrDragonFox/Elise) which is \~3 hour single-speaker English speech
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    * [`MrDragonFox/Elise`](https://huggingface.co/datasets/MrDragonFox/Elise) – an augmented version with **emotion tags** (e.g. \<sigh>, \<laughs>) embedded in the transcripts. These tags in angle brackets indicate expressions (laughter, sigh
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    * [`Jinsaryko/Elise`](https://huggingface.co/datasets/Jinsaryko/Elise) – base version with transcripts without special tags.
  • Network accessline 10361
    You can now run **Grok 2** (aka Grok 2.5), the 270B parameter model by xAI. Full precision requires **539GB**, while the Unsloth Dynamic 3-bit version shrinks size down to just **118GB** (a 75% reduction). GGUF: [Grok-2-GGUF](https://huggin
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    The **3-bit Q3\_K\_XL** model runs on a single **128GB Mac** or **24GB VRAM + 128GB RAM**, achieving **5+ tokens/s** inference. Thanks to the llama.cpp team and community for [supporting Grok 2](https://github.com/ggml-org/llama.cpp/pull/15
  • Network accessline 10365
    All uploads use Unsloth [Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) for SOTA 5-shot MMLU and KL Divergence performance, meaning you can run quantized Grok LLMs with minimal accuracy loss.
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    {% stepper %} {% step %} Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if
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    Install the specific `llama.cpp` PR for Grok 2 on [GitHub here](https://github.com/ggml-org/llama.cpp/pull/15539). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp cd llama.cpp && git fetch origin pull/15539/head:MASTER && git checkout MASTER && cd .. cmake llama.cpp -B llama.cpp/build \
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    See <https://docs.sglang.ai/get_started/install.html> for more details
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    <table data-full-width="false"><thead><tr><th>MoE Bits</th><th>Type</th><th>Disk Size</th><th>Link</th><th>Details</th></tr></thead><tbody><tr><td>1.78bit</td><td>IQ1_S</td><td>33.8GB</td><td><a href="https://huggingface.co/unsloth/Llama-4-
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    | MoE Bits | Type      | Disk Size | HF Link                                                                                             | | -------- | --------- | --------- | ----------------------------------------------------------------
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    | -------- | --------- | --------- | --------------------------------------------------------------------------------------------------- | | 1.78bit  | IQ1\_S    | 122GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-
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    | 1.78bit  | IQ1\_S    | 122GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_S)   |
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    | 1.93bit  | IQ1\_M    | 128GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_M)   |
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    | 2.42-bit | IQ2\_XXS  | 140GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ2_XXS) |
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    | 2.71-bit | Q2\_K\_XL | 151B      | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q2_K_XL) |
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    | 3.5-bit  | Q3\_K\_XL | 193GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q3_K_XL) |
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    | 4.5-bit  | Q4\_K\_XL | 243GB     | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-Q4_K_XL) |
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    * A BOS token of `<|begin_of_text|>` is auto added during tokenization (do NOT add it manually!)&#x20; * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow
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    * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow;">**suggested optional system prompt**</mark>, which is listed below:
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    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
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    2. Download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/Llama-4-Scout-17B-16
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    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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    git clone https://github.com/ggml-org/llama.cpp
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    Unsloth supports fine-tuning of DeepSeek-OCR. Since the default model isn’t fine-tunable, we added changes from the [Stranger Vision HF](https://huggingface.co/strangervisionhf) team, to then enable fine-tuning. As usual, Unsloth trains Dee
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    * DeepSeek-OCR: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb)
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    * DeepSeek-OCR: [Fine-tuning + Evaluation notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\)-Eval.ipynb) (A100)
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    You can now train OpenAI [gpt-oss](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune) with RL and GRPO via [Unsloth](https://github.com/unslothai/unsloth). Unsloth now offers the <mark style="background-color:$success;">**fast
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    Since reinforcement learning (RL) on gpt-oss isn't yet vLLM compatible, we had to rewrite the inference code from Transformers code to deliver 3x faster inference for gpt-oss at \~21 tokens/s. For BF16, Unsloth also achieves the fastest inf
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    * **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\
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    Unsloth is the only framework to support 4-bit RL for gpt-oss. All performance gains are due to Unsloth's unique [weight sharing](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#what-unsloth-offers-for-rl), [Flex Attenti
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    {% hint style="warning" %} Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and
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    Reminder: <mark style="background-color:$info;">**Flash Attention 3 (FA3) is**</mark> [<mark style="background-color:$info;">**unsuitable for gpt-oss**</mark>](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt
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    Inference is crucial in RL training, since we need it to generate candidate solutions before maximizing some reward function ([see here](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) for a more detailed explanation).
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    vLLM does not support RL for gpt-oss since it lacks BF16 training and LoRA support for gpt-oss. Without Unsloth, only training via full precision BF16 works, making <mark style="background-color:$warning;">memory use</mark> <mark style="bac
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    We had to change our implementation for attention sinks as [described here](https://docs.unsloth.ai/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) to allow generation to work with left padding. We had to get the logs
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    The harder part is batch generation. Sequences differ in length, so padding complicates mask creation. Flex Attention had a lot of [challenges](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) and dynamic mas
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    > *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665)
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    It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking).
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    <div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div>
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    In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation setting and showcase tangible solutions to
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    LLMs often struggle with tasks that involve complex environments. However, by applying [reinforcement learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) (RL) and designing a custom [reward function](https://docs.
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    RL can be adapted for tasks such as auto kernel or strategy creation. This tutorial shows how to train **gpt-oss** with [**GRPO**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#from-rlhf-ppo-to-grpo-and-rlvr) and Unslo
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    | [2048 notebook](https://colab.research.google.com/github/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb) (Official OpenAI example) | [Kernel generation notebook](https://colab.research.google.com/github/unslothai/notebo
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    Unsloth now supports NVIDIA’s Blackwell architecture GPUs, including RTX 50-series GPUs (5060–5090), RTX PRO 6000, and GPUS such as B200, B40, GB100, GB102 and more! You can read the official [NVIDIA blogpost here](https://developer.nvidia.
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    Unsloth is now compatible with every NVIDIA GPU from 2018+ including the [DGX Spark](https://docs.unsloth.ai/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth).
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    > **Our new** [**Docker image**](#docker) **supports Blackwell. Run the Docker image and start training!** [**Guide**](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth)
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    By the end of this tutorial, you will create a custom chatbot by **finetuning Llama-3** with [**Unsloth**](https://github.com/unslothai/unsloth) for free. It can run locally via [**Ollama**](https://github.com/ollama/ollama) on your PC, or
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    **Unsloth** makes finetuning much easier, and can automatically export the finetuned model to **Ollama** with integrated automatic `Modelfile` creation! If you need help, you can join our Discord server: <https://discord.com/invite/unsloth>
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    {% hint style="warning" %} **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You c
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    **If you’d like to copy or save the code, everything is available in our** [**Ollama Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)**. You can use it directly there or
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    [Unsloth](https://github.com/unslothai/unsloth) makes finetuning LLMs like Llama-3, Mistral, Phi-3 and Gemma 2x faster, use 70% less memory, and with no degradation in accuracy! We will be using Google Colab which provides a free GPU during
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    * [Ollama Llama-3 Alpaca](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb) (notebook which we will be using)
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    * [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing)
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    [Ollama ](https://github.com/ollama/ollama)allows you to run language models from your own computer in a quick and simple way! It quietly launches a program which can run a language model like Llama-3 in the background. If you suddenly want
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    Options include `True`, `False` and `"unsloth"`. We suggest `"unsloth"` since we reduce memory usage by an extra 30% and support extremely long context finetunes.You can read up here: <https://unsloth.ai/blog/long-context> for more details.
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    You can access the GPT4 version of the Alpaca dataset here: <https://huggingface.co/datasets/vicgalle/alpaca-gpt4>. An older first version of the dataset is here: <https://github.com/tatsu-lab/stanford_alpaca>. Below shows some examples of
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    To access the Titanic finetuning notebook or if you want to upload a CSV or Excel file, go here: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing>
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  • Network accessline 11223
    Or in the Titanic prediction task where you had to predict if a passenger died or survived in this Colab  notebook which includes CSV and Excel uploading: <https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharin
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  • Network accessline 11262
    We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hugging Face token via <https://huggingface
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  • Network accessline 11282
    Head over to <https://github.com/ggerganov/llama.cpp> to learn more about GGUF. We also have some manual instructions of how to export to GGUF if you want here: <https://github.com/unslothai/unsloth/wiki#manually-saving-to-gguf>
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    If you want to learn how to do reward modelling, do continued pretraining, export to vLLM or GGUF, do text completion, or learn more about finetuning tips and tricks, head over to our [Github](https://github.com/unslothai/unsloth#-finetune-
  • Network accessline 11330
    If you need any help on finetuning, you can also join our Discord server [here](https://discord.gg/unsloth). If you want help with Ollama, you can also join their server [here](https://discord.gg/ollama).
  • Network accessline 11334
    To access our Alpaca dataset example click [here](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing), and our CSV / Excel finetuning guide is [here](https://colab.research.google.com/drive/1VYkncZMfGFkeCE
  • Network accessline 11369
    ## https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp#L19
  • Network accessline 11371
    **URL:** llms-txt#https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp#l19
  • Network accessline 11396
    Google releases Gemma 3 with a new 270M model and the previous 1B, 4B, 12B, and 27B sizes. The 270M and 1B are text-only, while larger models handle both text and vision. We provide GGUFs, and a guide of how to run it effectively, and how t
  • Network accessline 11398
    {% hint style="success" %} **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co
  • Network accessline 11399
    **NEW Aug 14, 2025 Update:** Try our fine-tuning [Gemma 3 (270M) notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) and [GGUFs to run](https://huggingface.co/collections/unsloth/gemma-
  • Network accessline 11401
    Also see our [Gemma 3n Guide](https://docs.unsloth.ai/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune).
  • Network accessline 11408
    * Fine-tune Gemma 3 (4B) with vision support using our [free Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb)
  • Network accessline 11417
    | GGUF
  • Network accessline 11419
    | <ul><li><a href="https://huggingface.co/unsloth/gemma-3-270m-it-GGUF">270M</a> - new</li><li><a href="https://huggingface.co/unsloth/gemma-3-1b-it-GGUF">1B</a></li><li><a href="https://huggingface.co/unsloth/gemma-3-4b-it-GGUF">4B</a></li
  • Network accessline 11444
    To run the models on your phone, we recommend using any mobile app that can run GGUFs locally on edge devices like phones. After fine-tuning you can export it to GGUF then run it locally on your phone. Ensure your phone has enough RAM/power
  • Network accessline 11458
    1. Obtain the latest `llama.cpp` on [GitHub here](https://github.com/ggml-org/llama.cpp). You can follow the build instructions below as well. Change `-DGGML_CUDA=ON` to `-DGGML_CUDA=OFF` if you don't have a GPU or just want CPU inference.
  • Network accessline 11462
    3. **OR** download the model via (after installing `pip install huggingface_hub hf_transfer` ). You can choose Q4\_K\_M, or other quantized versions (like BF16 full precision). More versions at: <https://huggingface.co/unsloth/gemma-3-27b-i
  • Network accessline 11479
    apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh
  • Dangerous shell operationline 11481
    curl -fsSL https://ollama.com/install.sh | sh
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    curl -fsSL https://ollama.com/install.sh | sh
  • Network accessline 11491
    apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11492
    apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
  • Network accessline 11493
    git clone https://github.com/ggerganov/llama.cpp
  • Network accessline 11514
    At [Unsloth](https://app.gitbook.com/o/HpyELzcNe0topgVLGCZY/s/xhOjnexMCB3dmuQFQ2Zq/), our mission is to make AI as accurate and accessible as possible. Train, run, evaluate and save gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma LLMs 2
  • Network accessline 11518
    <a href="beginner-start-here" class="button primary">Get started</a> <a href="https://github.com/unslothai/unsloth" class="button secondary">Our GitHub</a>
  • Network accessline 11520
    [Credential-related source omitted]
  • Network accessline 11523
    {% column %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
  • Network accessline 11524
    {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) {% endcontent-ref %}
  • Network accessline 11525
    [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide)
  • Network accessline 11528
    {% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks)
  • Network accessline 11529
    [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks)
  • Network accessline 11532
    {% column %} {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models)
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    {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) {% endcontent-ref %}
  • Network accessline 11534
    [all-our-models](https://docs.unsloth.ai/get-started/all-our-models)
  • Network accessline 11537
    {% content-ref url="../models/tutorials-how-to-fine-tune-and-run-llms" %} [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms)
  • Network accessline 11538
    [tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms)
  • Network accessline 11543
    [Credential-related source omitted]
  • Network accessline 11545
    * Unsloth streamlines model training locally and on Colab/Kaggle, covering loading, quantization, training, evaluation, saving, exporting, and integration with inference engines like Ollama, llama.cpp, and vLLM. * We directly collaborate wi
  • Network accessline 11546
    * We directly collaborate with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_con
  • Network accessline 11547
    * Unsloth is the only training framework to support all model types:  [vision](https://docs.unsloth.ai/basics/vision-fine-tuning), [text-to-speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), BERT, [reinforcement l
  • Network accessline 11549
    * Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **8-bit** training. * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speec
  • Network accessline 11550
    * The most efficient RL library, using 80% less VRAM. Supports GRPO, GSPO etc. * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-n
  • Network accessline 11551
    * Supports **all models**: [TTS,](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning) multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more. Any model that works in transform
  • Network accessline 11552
    * **0% loss in accuracy** - no approximation methods - all exact. * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! * Unsloth supports Linux, Windows, Colab, Kagg
  • Network accessline 11553
    * [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming!
  • Network accessline 11554
    * Unsloth supports Linux, Windows, Colab, Kaggle, **NVIDIA** and [**AMD**](https://docs.unsloth.ai/new/fine-tuning-llms-on-amd-gpus-with-unsloth) & **Intel**. See:
  • Network accessline 11556
    {% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
  • Network accessline 11557
    [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements)
  • Network accessline 11562
    Use our official **Docker image**: `unsloth/unsloth`. Read our [**Docker guide**](https://docs.unsloth.ai/get-started/install-and-update/docker)**.**
  • Network accessline 11564
    For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/install-and-update/windows-installation).
  • Network accessline 11566
    {% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
  • Network accessline 11567
    [install-and-update](https://docs.unsloth.ai/get-started/install-and-update)
  • Network accessline 11572
    [**Fine-tuning** an LLM](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) customizes its behavior, enhances domain knowledge, and optimizes performance for specific tasks. By fine-tuning a pre-trained model (e.g. Llama-3.1-8B) on
  • Network accessline 11578
    [**Reinforcement Learning (RL)**](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) is where an "agent" learns to make decisions by interacting with an environment and receiving **feedback** in the form of **rewards** or
  • Network accessline 11592
    {% content-ref url="beginner-start-here/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    [faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me)
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    {% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide)
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    [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide)
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    [Credential-related source omitted]
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    Local training can be complex due to dependency hell or breaking environments. Unsloth’s [Docker image](https://hub.docker.com/r/unsloth/unsloth) can bypass these issues. No setup is needed: pull and run the image and start training.
  • Network accessline 11708
    * **Unsloth official Docker image:** [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth)
  • Network accessline 11712
    Unsloth’s Docker image is stable, up-to-date and works in [supported setups](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements#system-requirements) like Windows.
  • Network accessline 11728
    Install Docker via [Linux](https://docs.docker.com/engine/install/) or [Desktop](https://docs.docker.com/desktop/) (other).\
  • Network accessline 11729
    Then install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation):
  • Dangerous shell operationline 11732
    </strong>sudo apt-get update &#x26;&#x26; sudo apt-get install -y \
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    [Credential-related source omitted]
  • Network accessline 11744
    [**`unsloth/unsloth`**](https://hub.docker.com/r/unsloth/unsloth) is Unsloth's only Docker image. For [Blackwell](https://docs.unsloth.ai/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and 50-series GPUs, use this same im
  • Network accessline 11746
    [Credential-related source omitted]
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    Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth.
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    [Credential-related source omitted]
  • Network accessline 11757
    [Credential-related source omitted]
  • Network accessline 11762
    If you're new, follow our step-by-step [Fine-tuning Guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [RL Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or just save/copy any of our premade [no
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
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    [Credential-related source omitted]
  • Network accessline 11848
    It's the reason models learn to modify unit tests to pass coding challenges, and these are critical blockers for real world deployment. Some other good examples are from [Wikipedia](https://en.wikipedia.org/wiki/Reward_hacking).
  • Network accessline 11850
    <div align="center"><figure><img src="https://i.pinimg.com/originals/55/e0/1b/55e01b94a9c5546b61b59ae300811c83.gif" alt="" width="188"><figcaption></figcaption></figure></div>
  • Network accessline 11852
    **Can you counter reward hacking? Yes!** In our [free gpt-oss RL notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) we explore how to counter reward hacking in a code generation s
  • Network accessline 11876
    Unsloth works on Linux, Windows, NVIDIA, AMD, Google Colab and more. See our [system requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements).
  • Network accessline 11918
    See <https://docs.vllm.ai/en/stable/getting_started/installation> for more details
  • Access to secretsline 11960
    [Credential-related source omitted]
  • Network accessline 12004
    {% hint style="warning" %} Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install).
  • Network accessline 12005
    Do **NOT** use this if you have [Conda](https://docs.unsloth.ai/get-started/install-and-update/conda-install).
  • Network accessline 12029
    pip uninstall unsloth unsloth_zoo -y && pip install --no-deps git+https://github.com/unslothai/unsloth_zoo.git && pip install --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 12034
    pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
  • Network accessline 12035
    pip install --upgrade --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth-zoo.git
  • Network accessline 12040
    pip install --upgrade pip pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
  • Network accessline 12041
    pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"

skills/mlops/vector-databases/chroma/SKILL.md

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    [Credential-related source omitted]
  • Network accessline 403
    - **GitHub**: https://github.com/chroma-core/chroma ⭐ 24,300+
  • Network accessline 404
    - **Docs**: https://docs.trychroma.com
  • Network accessline 405
    - **Discord**: https://discord.gg/MMeYNTmh3x

skills/mlops/vector-databases/chroma/references/integration.md

  • Network accessline 38
    - **Docs**: https://docs.trychroma.com

skills/mlops/vector-databases/faiss/SKILL.md

  • Network accessline 220
    - **GitHub**: https://github.com/facebookresearch/faiss ⭐ 31,700+
  • Network accessline 221
    - **Wiki**: https://github.com/facebookresearch/faiss/wiki

skills/mlops/vector-databases/faiss/references/index_types.md

  • Network accessline 279
    - **Wiki**: https://github.com/facebookresearch/faiss/wiki
  • Network accessline 280
    - **Paper**: https://arxiv.org/abs/1702.08734

skills/mlops/vector-databases/pinecone/SKILL.md

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    [Credential-related source omitted]
  • Network accessline 356
    - **Website**: https://www.pinecone.io
  • Network accessline 357
    - **Docs**: https://docs.pinecone.io
  • Network accessline 358
    - **Console**: https://app.pinecone.io
  • Network accessline 359
    - **Pricing**: https://www.pinecone.io/pricing

skills/mlops/vector-databases/pinecone/references/deployment.md

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    - **Docs**: https://docs.pinecone.io
  • Network accessline 181
    - **Console**: https://app.pinecone.io

skills/mlops/vector-databases/qdrant/SKILL.md

  • Network accessline 288
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") vectorstore = Qdrant.from_documents(documents, embeddings, url="http://localhost:6333", collection_name="docs")
  • Network accessline 289
    vectorstore = Qdrant.from_documents(documents, embeddings, url="http://localhost:6333", collection_name="docs")
  • Network accessline 418
    # Connect to Qdrant Cloud client = QdrantClient(     url="https://your-cluster.cloud.qdrant.io",
  • Network accessline 419
    [Credential-related source omitted]
  • Network accessline 420
    url="https://your-cluster.cloud.qdrant.io",
  • Access to secretsline 421
    [Credential-related source omitted]
  • Network accessline 491
    - **GitHub**: https://github.com/qdrant/qdrant (22k+ stars)
  • Network accessline 492
    - **Docs**: https://qdrant.tech/documentation/
  • Network accessline 493
    - **Python Client**: https://github.com/qdrant/qdrant-client
  • Network accessline 494
    - **Cloud**: https://cloud.qdrant.io

skills/mlops/vector-databases/qdrant/references/advanced-usage.md

  • Network accessline 36
    - QDRANT__CLUSTER__ENABLED=true       - QDRANT__CLUSTER__P2P__PORT=6335       - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
  • Network accessline 37
    - QDRANT__CLUSTER__P2P__PORT=6335       - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335     depends_on:
  • Network accessline 38
    - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
  • Network accessline 51
    - QDRANT__CLUSTER__ENABLED=true       - QDRANT__CLUSTER__P2P__PORT=6335       - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
  • Network accessline 52
    - QDRANT__CLUSTER__P2P__PORT=6335       - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335     depends_on:
  • Network accessline 53
    - QDRANT__CLUSTER__BOOTSTRAP=http://qdrant-node-1:6335
  • Network accessline 447
    response = requests.put(
  • Network accessline 448
    "http://localhost:6333/collections/documents/snapshots/recover",

skills/mlops/vector-databases/qdrant/references/troubleshooting.md

  • Dangerous shell operationline 12
    sudo systemctl start docker
  • Network accessline 62
    docker ps | grep qdrant curl http://localhost:6333/healthz
  • Network accessline 63
    curl http://localhost:6333/healthz
  • Network accessline 114
    # Qdrant Cloud client = QdrantClient(     url="https://cluster.cloud.qdrant.io",
  • Network accessline 115
    [Credential-related source omitted]
  • Network accessline 116
    url="https://cluster.cloud.qdrant.io",
  • Access to secretsline 117
    [Credential-related source omitted]
  • Network accessline 459
    # Check Raft state curl http://localhost:6333/cluster
  • Network accessline 460
    curl http://localhost:6333/cluster
  • Network accessline 469
    # Force leader election curl -X POST http://localhost:6333/cluster/recover
  • Network accessline 470
    curl -X POST http://localhost:6333/cluster/recover
  • Network accessline 619
    1. **Documentation**: https://qdrant.tech/documentation/
  • Network accessline 620
    2. **GitHub Issues**: https://github.com/qdrant/qdrant/issues
  • Network accessline 621
    3. **Discord**: https://discord.gg/qdrant
  • Network accessline 626
    Include: - Qdrant version: `curl http://localhost:6333/`
  • Network accessline 627
    - Qdrant version: `curl http://localhost:6333/`

skills/productivity/google-workspace/SKILL.md

  • Network accessline 8
    gauss:     tags: [Google, Gmail, Calendar, Drive, Sheets, Docs, Contacts, Email, OAuth]     homepage: https://github.com/NousResearch/gauss-agent
  • Network accessline 9
    tags: [Google, Gmail, Calendar, Drive, Sheets, Docs, Contacts, Email, OAuth]     homepage: https://github.com/NousResearch/gauss-agent     related_skills: [himalaya]
  • Network accessline 10
    homepage: https://github.com/NousResearch/gauss-agent
  • Network accessline 73
    > You need a Google Cloud OAuth client. This is a one-time setup: > > 1. Go to https://console.cloud.google.com/apis/credentials
  • Network accessline 74
    > > 1. Go to https://console.cloud.google.com/apis/credentials > 2. Create a project (or use an existing one)
  • Network accessline 75
    > 1. Go to https://console.cloud.google.com/apis/credentials
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    [Credential-related source omitted]
  • Network accessline 104
    The user will paste back either a URL like `http://localhost:1/?code=4/0A...&scope=...`

skills/productivity/google-workspace/scripts/google_api.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 34
    SCOPES = [     "https://www.googleapis.com/auth/gmail.readonly",
  • Network accessline 35
    "https://www.googleapis.com/auth/gmail.readonly",
  • Network accessline 36
    "https://www.googleapis.com/auth/gmail.send",
  • Network accessline 37
    "https://www.googleapis.com/auth/gmail.modify",
  • Network accessline 38
    "https://www.googleapis.com/auth/calendar",
  • Network accessline 39
    "https://www.googleapis.com/auth/drive.readonly",
  • Network accessline 40
    "https://www.googleapis.com/auth/contacts.readonly",
  • Network accessline 41
    "https://www.googleapis.com/auth/spreadsheets",
  • Network accessline 42
    "https://www.googleapis.com/auth/documents.readonly",

skills/productivity/google-workspace/scripts/setup.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Access to secretsline 17
    [Credential-related source omitted]
  • Access to secretsline 33
    [Credential-related source omitted]
  • Network accessline 36
    SCOPES = [     "https://www.googleapis.com/auth/gmail.readonly",
  • Network accessline 37
    "https://www.googleapis.com/auth/gmail.readonly",
  • Network accessline 38
    "https://www.googleapis.com/auth/gmail.send",
  • Network accessline 39
    "https://www.googleapis.com/auth/gmail.modify",
  • Network accessline 40
    "https://www.googleapis.com/auth/calendar",
  • Network accessline 41
    "https://www.googleapis.com/auth/drive.readonly",
  • Network accessline 42
    "https://www.googleapis.com/auth/contacts.readonly",
  • Network accessline 43
    "https://www.googleapis.com/auth/spreadsheets",
  • Network accessline 44
    "https://www.googleapis.com/auth/documents.readonly",
  • Network accessline 52
    REDIRECT_URI = "http://localhost:1"
  • Access to secretsline 123
    [Credential-related source omitted]
  • Access to secretsline 124
    [Credential-related source omitted]
  • Network accessline 136
    if "installed" not in data and "web" not in data:         print("ERROR: Not a Google OAuth client secret file (missing 'installed' key).")         print("Download the correct file from: https://console.cloud.google.com/apis/credentials")
  • Network accessline 137
    print("ERROR: Not a Google OAuth client secret file (missing 'installed' key).")         print("Download the correct file from: https://console.cloud.google.com/apis/credentials")         sys.exit(1)
  • Network accessline 138
    print("Download the correct file from: https://console.cloud.google.com/apis/credentials")
  • Network accessline 272
    import urllib.request         urllib.request.urlopen(
  • Network accessline 273
    urllib.request.urlopen(
  • Network accessline 274
    urllib.request.Request(
  • Network accessline 275
    [Credential-related source omitted]
  • Access to secretsline 293
    [Credential-related source omitted]
  • Access to secretsline 302
    [Credential-related source omitted]
  • Access to secretsline 303
    [Credential-related source omitted]

skills/productivity/linear/SKILL.md

  • Network accessline 1
    --- name: linear description: Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies.
  • Network accessline 2
    name: linear description: Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies. version: 1.0.0
  • Network accessline 3
    description: Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies.
  • Access to secretsline 8
    [Credential-related source omitted]
  • Access to secretsline 22
    [Credential-related source omitted]
  • Network accessline 26
    [Credential-related source omitted]
  • Access to secretsline 27
    [Credential-related source omitted]
  • Network accessline 31
    Base curl pattern:
  • Dangerous shell operationline 33
    [Credential-related source omitted]
  • Network accessline 33
    [Credential-related source omitted]
  • Access to secretsline 33
    [Credential-related source omitted]
  • Access to secretsline 34
    [Credential-related source omitted]
  • Dangerous shell operationline 60
    [Credential-related source omitted]
  • Network accessline 60
    [Credential-related source omitted]
  • Access to secretsline 60
    [Credential-related source omitted]
  • Access to secretsline 61
    [Credential-related source omitted]
  • Dangerous shell operationline 68
    [Credential-related source omitted]
  • Network accessline 68
    [Credential-related source omitted]
  • Access to secretsline 68
    [Credential-related source omitted]
  • Access to secretsline 69
    [Credential-related source omitted]
  • Dangerous shell operationline 76
    [Credential-related source omitted]
  • Network accessline 76
    [Credential-related source omitted]
  • Access to secretsline 76
    [Credential-related source omitted]
  • Access to secretsline 77
    [Credential-related source omitted]
  • Dangerous shell operationline 84
    [Credential-related source omitted]
  • Network accessline 84
    [Credential-related source omitted]
  • Access to secretsline 84
    [Credential-related source omitted]
  • Access to secretsline 85
    [Credential-related source omitted]
  • Dangerous shell operationline 92
    [Credential-related source omitted]
  • Network accessline 92
    [Credential-related source omitted]
  • Access to secretsline 92
    [Credential-related source omitted]
  • Access to secretsline 93
    [Credential-related source omitted]
  • Dangerous shell operationline 100
    [Credential-related source omitted]
  • Network accessline 100
    [Credential-related source omitted]
  • Access to secretsline 100
    [Credential-related source omitted]
  • Access to secretsline 101
    [Credential-related source omitted]
  • Dangerous shell operationline 108
    [Credential-related source omitted]
  • Network accessline 108
    [Credential-related source omitted]
  • Access to secretsline 108
    [Credential-related source omitted]
  • Access to secretsline 109
    [Credential-related source omitted]
  • Dangerous shell operationline 116
    [Credential-related source omitted]
  • Network accessline 116
    [Credential-related source omitted]
  • Access to secretsline 116
    [Credential-related source omitted]
  • Access to secretsline 117
    [Credential-related source omitted]
  • Dangerous shell operationline 124
    [Credential-related source omitted]
  • Network accessline 124
    [Credential-related source omitted]
  • Access to secretsline 124
    [Credential-related source omitted]
  • Access to secretsline 125
    [Credential-related source omitted]
  • Dangerous shell operationline 132
    [Credential-related source omitted]
  • Network accessline 132
    [Credential-related source omitted]
  • Access to secretsline 132
    [Credential-related source omitted]
  • Access to secretsline 133
    [Credential-related source omitted]
  • Dangerous shell operationline 140
    [Credential-related source omitted]
  • Network accessline 140
    [Credential-related source omitted]
  • Access to secretsline 140
    [Credential-related source omitted]
  • Access to secretsline 141
    [Credential-related source omitted]
  • Dangerous shell operationline 148
    [Credential-related source omitted]
  • Network accessline 148
    [Credential-related source omitted]
  • Access to secretsline 148
    [Credential-related source omitted]
  • Access to secretsline 149
    [Credential-related source omitted]
  • Network accessline 158
    [Credential-related source omitted]
  • Access to secretsline 158
    [Credential-related source omitted]
  • Access to secretsline 159
    [Credential-related source omitted]
  • Dangerous shell operationline 177
    [Credential-related source omitted]
  • Network accessline 177
    [Credential-related source omitted]
  • Access to secretsline 177
    [Credential-related source omitted]
  • Access to secretsline 178
    [Credential-related source omitted]
  • Dangerous shell operationline 185
    [Credential-related source omitted]
  • Network accessline 185
    [Credential-related source omitted]
  • Access to secretsline 185
    [Credential-related source omitted]
  • Access to secretsline 186
    [Credential-related source omitted]
  • Dangerous shell operationline 193
    [Credential-related source omitted]
  • Network accessline 193
    [Credential-related source omitted]
  • Access to secretsline 193
    [Credential-related source omitted]
  • Access to secretsline 194
    [Credential-related source omitted]
  • Dangerous shell operationline 201
    [Credential-related source omitted]
  • Network accessline 201
    [Credential-related source omitted]
  • Access to secretsline 201
    [Credential-related source omitted]
  • Access to secretsline 202
    [Credential-related source omitted]
  • Dangerous shell operationline 209
    [Credential-related source omitted]
  • Network accessline 209
    [Credential-related source omitted]
  • Access to secretsline 209
    [Credential-related source omitted]
  • Access to secretsline 210
    [Credential-related source omitted]
  • Dangerous shell operationline 217
    [Credential-related source omitted]
  • Network accessline 217
    [Credential-related source omitted]
  • Access to secretsline 217
    [Credential-related source omitted]
  • Access to secretsline 218
    [Credential-related source omitted]
  • Dangerous shell operationline 225
    [Credential-related source omitted]
  • Network accessline 225
    [Credential-related source omitted]
  • Access to secretsline 225
    [Credential-related source omitted]
  • Access to secretsline 226
    [Credential-related source omitted]
  • Network accessline 233
    [Credential-related source omitted]
  • Access to secretsline 233
    [Credential-related source omitted]
  • Access to secretsline 234
    [Credential-related source omitted]
  • Network accessline 253
    # First page curl -s -X POST https://api.linear.app/graphql \
  • Dangerous shell operationline 254
    [Credential-related source omitted]
  • Network accessline 254
    [Credential-related source omitted]
  • Access to secretsline 254
    [Credential-related source omitted]
  • Access to secretsline 255
    [Credential-related source omitted]
  • Network accessline 259
    # Next page — use endCursor from previous response curl -s -X POST https://api.linear.app/graphql \
  • Dangerous shell operationline 260
    [Credential-related source omitted]
  • Network accessline 260
    [Credential-related source omitted]
  • Access to secretsline 260
    [Credential-related source omitted]
  • Access to secretsline 261
    [Credential-related source omitted]

skills/productivity/nano-pdf/SKILL.md

  • Network accessline 8
    gauss:     tags: [PDF, Documents, Editing, NLP, Productivity]     homepage: https://pypi.org/project/nano-pdf/
  • Network accessline 9
    tags: [PDF, Documents, Editing, NLP, Productivity]     homepage: https://pypi.org/project/nano-pdf/ ---
  • Network accessline 10
    homepage: https://pypi.org/project/nano-pdf/

skills/productivity/notion/SKILL.md

  • Network accessline 8
    gauss:     tags: [Notion, Productivity, Notes, Database, API]     homepage: https://developers.notion.com
  • Network accessline 9
    tags: [Notion, Productivity, Notes, Database, API]     homepage: https://developers.notion.com prerequisites:
  • Network accessline 10
    homepage: https://developers.notion.com
  • Access to secretsline 12
    [Credential-related source omitted]
  • Network accessline 17
    Use the Notion API via curl to create, read, update pages, databases (data sources), and blocks. No extra tools needed — just curl and a Notion API key.
  • Network accessline 21
    1. Create an integration at https://notion.so/my-integrations
  • Access to secretsline 25
    [Credential-related source omitted]
  • Network accessline 34
    [Credential-related source omitted]
  • Access to secretsline 34
    [Credential-related source omitted]
  • Access to secretsline 35
    [Credential-related source omitted]
  • Network accessline 47
    [Credential-related source omitted]
  • Access to secretsline 47
    [Credential-related source omitted]
  • Access to secretsline 48
    [Credential-related source omitted]
  • Network accessline 57
    [Credential-related source omitted]
  • Access to secretsline 57
    [Credential-related source omitted]
  • Access to secretsline 58
    [Credential-related source omitted]
  • Network accessline 65
    [Credential-related source omitted]
  • Access to secretsline 65
    [Credential-related source omitted]
  • Access to secretsline 66
    [Credential-related source omitted]
  • Network accessline 73
    [Credential-related source omitted]
  • Access to secretsline 73
    [Credential-related source omitted]
  • Access to secretsline 74
    [Credential-related source omitted]
  • Network accessline 89
    [Credential-related source omitted]
  • Access to secretsline 89
    [Credential-related source omitted]
  • Access to secretsline 90
    [Credential-related source omitted]
  • Network accessline 102
    [Credential-related source omitted]
  • Access to secretsline 102
    [Credential-related source omitted]
  • Access to secretsline 103
    [Credential-related source omitted]
  • Network accessline 120
    [Credential-related source omitted]
  • Access to secretsline 120
    [Credential-related source omitted]
  • Access to secretsline 121
    [Credential-related source omitted]
  • Network accessline 130
    [Credential-related source omitted]
  • Access to secretsline 130
    [Credential-related source omitted]
  • Access to secretsline 131
    [Credential-related source omitted]
  • Network accessline 150
    - **Checkbox:** `{"checkbox": true}` - **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}`
  • Network accessline 151
    - **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}` - **Email:** `{"email": "user@example.com"}`
  • Network accessline 152
    - **URL:** `{"url": "https://..."}`
  • Network accessline 168
    - The API cannot set database view filters — that's UI-only - Use `is_inline: true` when creating data sources to embed them in pages - Add `-s` flag to curl to suppress progress bars (cleaner output for Gauss)
  • Network accessline 169
    - Use `is_inline: true` when creating data sources to embed them in pages - Add `-s` flag to curl to suppress progress bars (cleaner output for Gauss) - Pipe output through `jq` for readable JSON: `... | jq '.results[0].properties'`
  • Network accessline 170
    - Add `-s` flag to curl to suppress progress bars (cleaner output for Gauss)

skills/productivity/notion/references/block-types.md

  • Network accessline 78
    {"type": "bookmark", "bookmark": {"url": "https://example.com"}}
  • Network accessline 84
    {"type": "image", "image": {"type": "external", "external": {"url": "https://example.com/photo.png"}}}
  • Network accessline 112
    *Contributed by [@dogiladeveloper](https://github.com/dogiladeveloper)*

skills/productivity/ocr-and-documents/SKILL.md

  • Network accessline 24
    web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
  • Network accessline 25
    web_extract(urls=["https://example.com/report.pdf"])
  • Network accessline 115
    # Abstract only (fast) web_extract(urls=["https://arxiv.org/abs/2402.03300"])
  • Network accessline 116
    web_extract(urls=["https://arxiv.org/abs/2402.03300"])
  • Network accessline 118
    # Full paper web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
  • Network accessline 119
    web_extract(urls=["https://arxiv.org/pdf/2402.03300"])

skills/productivity/ocr-and-documents/scripts/extract_marker.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/productivity/ocr-and-documents/scripts/extract_pymupdf.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/productivity/powerpoint/LICENSE.txt

  • Network accessline 6
    exists, use is governed by Anthropic's Consumer Terms of Service or Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms
  • Network accessline 7
    Commercial Terms of Service, as applicable: https://www.anthropic.com/legal/consumer-terms https://www.anthropic.com/legal/commercial-terms
  • Network accessline 8
    https://www.anthropic.com/legal/consumer-terms
  • Network accessline 9
    https://www.anthropic.com/legal/commercial-terms

skills/productivity/powerpoint/pptxgenjs.md

  • Network accessline 145
    // From URL slide.addImage({ path: "https://example.com/image.jpg", x: 1, y: 1, w: 5, h: 3 });
  • Network accessline 146
    slide.addImage({ path: "https://example.com/image.jpg", x: 1, y: 1, w: 5, h: 3 });
  • Network accessline 162
    flipV: false,            // Vertical flip   altText: "Description",  // Accessibility   hyperlink: { url: "https://example.com" }
  • Network accessline 163
    altText: "Description",  // Accessibility   hyperlink: { url: "https://example.com" } });
  • Network accessline 164
    hyperlink: { url: "https://example.com" }
  • Network accessline 257
    // Image from URL slide.background = { path: "https://example.com/bg.jpg" };
  • Network accessline 258
    slide.background = { path: "https://example.com/bg.jpg" };

skills/productivity/powerpoint/scripts/__init__.py

  • Executable scriptline 1
    Executable file

skills/productivity/powerpoint/scripts/add_slide.py

  • Executable scriptline 1
    Executable file
  • Network accessline 48
    slide_xml = '''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <p:sld xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:p="http://sche
  • Network accessline 49
    <p:sld xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main">
  • Network accessline 73
    rels_dir.mkdir(exist_ok=True)     rels_xml = f'''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
  • Network accessline 74
    rels_xml = f'''<?xml version="1.0" encoding="UTF-8" standalone="yes"?> <Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">   <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/200
  • Network accessline 75
    <Relationships xmlns="http://schemas.openxmlformats.org/package/2006/relationships">
  • Network accessline 76
    <Relationship Id="rId1" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/slideLayout" Target="../slideLayouts/{layout_file}"/>
  • Network accessline 149
    new_rel = f'<Relationship Id="{rid}" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/slide" Target="slides/{dest}"/>'

skills/productivity/powerpoint/scripts/clean.py

  • Executable scriptline 1
    Executable file

skills/productivity/powerpoint/scripts/office/helpers/__init__.py

  • Executable scriptline 1
    Executable file

skills/productivity/powerpoint/scripts/office/helpers/merge_runs.py

  • Executable scriptline 1
    Executable file

skills/productivity/powerpoint/scripts/office/helpers/simplify_redlines.py

  • Executable scriptline 1
    Executable file
  • Network accessline 19
    WORD_NS = "http://schemas.openxmlformats.org/wordprocessingml/2006/main"

skills/productivity/powerpoint/scripts/office/pack.py

  • Executable scriptline 1
    Executable file

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 4
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 5
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/chart"
  • Network accessline 6
    xmlns:cdr="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing"
  • Network accessline 7
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 8
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chart"
  • Network accessline 9
    elementFormDefault="qualified" attributeFormDefault="unqualified" blockDefault="#all">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 10
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 11
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 12
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 13
    schemaLocation="dml-main.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing"     schemaLocation="dml-chartDrawing.xsd"/>
  • Network accessline 14
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing"
  • Network accessline 15
    schemaLocation="dml-chartDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 16
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 4
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/chartDrawing"
  • Network accessline 6
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/diagram"
  • Network accessline 4
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 5
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 6
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 7
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/diagram"
  • Network accessline 8
    elementFormDefault="qualified" attributeFormDefault="unqualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 9
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 10
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 11
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 12
    schemaLocation="dml-main.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 13
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"
  • Network accessline 4
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 5
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 6
    elementFormDefault="qualified"   targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas">   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 7
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas">
  • Network accessline 8
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 4
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 5
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 6
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 7
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 8
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 9
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 10
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 11
    schemaLocation="shared-commonSimpleTypes.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram"     schemaLocation="dml-diagram.xsd"/>
  • Network accessline 12
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/diagram"
  • Network accessline 13
    schemaLocation="dml-diagram.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart"     schemaLocation="dml-chart.xsd"/>
  • Network accessline 14
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/chart"
  • Network accessline 15
    schemaLocation="dml-chart.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture"     schemaLocation="dml-picture.xsd"/>
  • Network accessline 16
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture"
  • Network accessline 17
    schemaLocation="dml-picture.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"     schemaLocation="dml-lockedCanvas.xsd"/>
  • Network accessline 18
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/lockedCanvas"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/picture"
  • Network accessline 4
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main" elementFormDefault="qualified"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/picture">
  • Network accessline 6
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 4
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
  • Network accessline 5
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 6
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
  • Network accessline 7
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 8
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 9
    schemaLocation="dml-main.xsd"/>   <xsd:import schemaLocation="shared-relationshipReference.xsd"     namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>
  • Network accessline 10
    <xsd:import schemaLocation="shared-relationshipReference.xsd"     namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>   <xsd:element name="from" type="CT_Marker"/>
  • Network accessline 11
    namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"/>

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 4
    xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 5
    xmlns:dpct="http://schemas.openxmlformats.org/drawingml/2006/picture"
  • Network accessline 6
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 7
    xmlns="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing"
  • Network accessline 8
    targetNamespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing"
  • Network accessline 9
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 10
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 11
    schemaLocation="dml-main.xsd"/>   <xsd:import schemaLocation="wml.xsd"     namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/>
  • Network accessline 12
    <xsd:import schemaLocation="wml.xsd"     namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture"
  • Network accessline 13
    namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"/>
  • Network accessline 14
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/picture"
  • Network accessline 15
    schemaLocation="dml-picture.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 16
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/pml.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/presentationml/2006/main"
  • Network accessline 4
    xmlns:p="http://schemas.openxmlformats.org/presentationml/2006/main"
  • Network accessline 5
    xmlns:a="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 6
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 7
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 8
    elementFormDefault="qualified"   targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 9
    targetNamespace="http://schemas.openxmlformats.org/presentationml/2006/main">
  • Network accessline 10
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 11
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"     schemaLocation="dml-main.xsd"/>
  • Network accessline 12
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main"
  • Network accessline 13
    schemaLocation="dml-main.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 14
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/characteristics"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/characteristics"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography"
  • Network accessline 4
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/bibliography"
  • Network accessline 6
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/customXml"
  • Network accessline 4
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/customXml"
  • Network accessline 6
    elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties"
  • Network accessline 4
    xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
  • Network accessline 5
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 6
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/custom-properties"
  • Network accessline 7
    blockDefault="#all" elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"     schemaLocation="shared-documentPropertiesVariantTypes.xsd"/>
  • Network accessline 8
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
  • Network accessline 9
    schemaLocation="shared-documentPropertiesVariantTypes.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 10
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties"
  • Network accessline 4
    xmlns:vt="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/extended-properties"
  • Network accessline 6
    elementFormDefault="qualified" blockDefault="#all">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"     schemaLocation="shared-documentPropertiesVariantTypes.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
  • Network accessline 4
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 5
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/docPropsVTypes"
  • Network accessline 6
    blockDefault="#all" elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/math"
  • Network accessline 4
    xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"
  • Network accessline 5
    xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 6
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 7
    elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all"   targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math">   <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/
  • Network accessline 8
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/math">
  • Network accessline 9
    <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 10
    schemaLocation="wml.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 11
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 12
    schemaLocation="shared-commonSimpleTypes.xsd"/>   <xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/>   <xsd:simpleType name="ST_Integer255">
  • Network accessline 13
    <xsd:import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="xml.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 4
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 5
    elementFormDefault="qualified"   targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"   blockDefault="#all">
  • Network accessline 6
    targetNamespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/sml.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main"
  • Network accessline 4
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 5
    xmlns:xdr="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
  • Network accessline 6
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 7
    targetNamespace="http://schemas.openxmlformats.org/spreadsheetml/2006/main"
  • Network accessline 8
    elementFormDefault="qualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 9
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 10
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 11
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 12
    schemaLocation="shared-commonSimpleTypes.xsd"/>   <xsd:import      namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"
  • Network accessline 13
    <xsd:import      namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"     schemaLocation="dml-spreadsheetDrawing.xsd"/>
  • Network accessline 14
    namespace="http://schemas.openxmlformats.org/drawingml/2006/spreadsheetDrawing"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns="urn:schemas-microsoft-com:vml"
  • Network accessline 3
    xmlns:pvml="urn:schemas-microsoft-com:office:powerpoint"   xmlns:o="urn:schemas-microsoft-com:office:office"   xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 4
    xmlns:o="urn:schemas-microsoft-com:office:office"   xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"   xmlns:w10="urn:schemas-microsoft-com:office:word"
  • Network accessline 5
    xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 6
    xmlns:w10="urn:schemas-microsoft-com:office:word"   xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"   xmlns:x="urn:schemas-microsoft-com:office:excel"
  • Network accessline 7
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 8
    xmlns:x="urn:schemas-microsoft-com:office:excel"   xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"   targetNamespace="urn:schemas-microsoft-com:vml" elementFormDefault="qualified"
  • Network accessline 9
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 12
    <xsd:import namespace="urn:schemas-microsoft-com:office:office"     schemaLocation="vml-officeDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 13
    schemaLocation="vml-officeDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"     schemaLocation="wml.xsd"/>
  • Network accessline 14
    <xsd:import namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 16
    <xsd:import namespace="urn:schemas-microsoft-com:office:word"     schemaLocation="vml-wordprocessingDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 17
    schemaLocation="vml-wordprocessingDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 18
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 22
    <xsd:import namespace="urn:schemas-microsoft-com:office:powerpoint"     schemaLocation="vml-presentationDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 23
    schemaLocation="vml-presentationDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 24
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="urn:schemas-microsoft-com:office:office" xmlns:v="urn:schemas-microsoft-com:vml"   xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"   xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedT
  • Network accessline 4
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 5
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 7
    attributeFormDefault="unqualified">   <xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 8
    <xsd:import namespace="urn:schemas-microsoft-com:vml" schemaLocation="vml-main.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 9
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 10
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 11
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns="urn:schemas-microsoft-com:office:excel"   xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"   targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified"
  • Network accessline 4
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 5
    targetNamespace="urn:schemas-microsoft-com:office:excel" elementFormDefault="qualified"   attributeFormDefault="unqualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 6
    attributeFormDefault="unqualified">   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 7
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/wml.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 2
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 3
    xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"
  • Network accessline 4
    xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 5
    xmlns:sl="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
  • Network accessline 6
    xmlns:wp="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing"
  • Network accessline 7
    xmlns="http://schemas.openxmlformats.org/wordprocessingml/2006/main"
  • Network accessline 8
    xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 9
    xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 10
    elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all"   targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main">   <xsd:import namespace="http://schemas.openxmlformats.org/markup-compatib
  • Network accessline 11
    targetNamespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main">
  • Network accessline 12
    <xsd:import namespace="http://schemas.openxmlformats.org/markup-compatibility/2006" schemaLocation="../mce/mc.xsd"/>
  • Network accessline 13
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/wordprocessingDrawing"
  • Network accessline 14
    schemaLocation="dml-wordprocessingDrawing.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math"     schemaLocation="shared-math.xsd"/>
  • Network accessline 15
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/math"
  • Network accessline 16
    schemaLocation="shared-math.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"     schemaLocation="shared-relationshipReference.xsd"/>
  • Network accessline 17
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships"
  • Network accessline 18
    schemaLocation="shared-relationshipReference.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"     schemaLocation="shared-commonSimpleTypes.xsd"/>
  • Network accessline 19
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes"
  • Network accessline 20
    schemaLocation="shared-commonSimpleTypes.xsd"/>   <xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"     schemaLocation="shared-customXmlSchemaProperties.xsd"/>
  • Network accessline 21
    <xsd:import namespace="http://schemas.openxmlformats.org/schemaLibrary/2006/main"
  • Network accessline 22
    schemaLocation="shared-customXmlSchemaProperties.xsd"/>   <xsd:import namespace="http://www.w3.org/XML/1998/namespace"/>   <xsd:complexType name="CT_Empty"/>
  • Network accessline 23
    <xsd:import namespace="http://www.w3.org/XML/1998/namespace"/>

skills/productivity/powerpoint/scripts/office/schemas/ISO-IEC29500-4_2016/xml.xsd

  • Network accessline 1
    <?xml version='1.0'?> <xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en">
  • Network accessline 2
    <xs:schema targetNamespace="http://www.w3.org/XML/1998/namespace" xmlns:xs="http://www.w3.org/2001/XMLSchema" xml:lang="en">
  • Network accessline 4
    <xs:annotation>   <xs:documentation>    See http://www.w3.org/XML/1998/namespace.html and
  • Network accessline 5
    <xs:documentation>    See http://www.w3.org/XML/1998/namespace.html and    http://www.w3.org/TR/REC-xml for information about this namespace.
  • Network accessline 6
    See http://www.w3.org/XML/1998/namespace.html and
  • Network accessline 7
    http://www.w3.org/TR/REC-xml for information about this namespace.
  • Network accessline 55
    &lt;schema . . .>          . . .          &lt;import namespace="http://www.w3.org/XML/1998/namespace"
  • Network accessline 56
    . . .          &lt;import namespace="http://www.w3.org/XML/1998/namespace"                     schemaLocation="http://www.w3.org/2001/03/xml.xsd"/>
  • Network accessline 57
    &lt;import namespace="http://www.w3.org/XML/1998/namespace"
  • Network accessline 58
    schemaLocation="http://www.w3.org/2001/03/xml.xsd"/>
  • Network accessline 72
    <xs:documentation>In keeping with the XML Schema WG's standard versioning    policy, this schema document will persist at    http://www.w3.org/2001/03/xml.xsd.
  • Network accessline 73
    policy, this schema document will persist at    http://www.w3.org/2001/03/xml.xsd.    At the date of issue it can also be found at
  • Network accessline 74
    http://www.w3.org/2001/03/xml.xsd.
  • Network accessline 75
    At the date of issue it can also be found at    http://www.w3.org/2001/xml.xsd.    The schema document at that URI may however change in the future,
  • Network accessline 76
    http://www.w3.org/2001/xml.xsd.
  • Network accessline 79
    itself.  In other words, if the XML Schema namespace changes, the version    of this document at    http://www.w3.org/2001/xml.xsd will change
  • Network accessline 80
    of this document at    http://www.w3.org/2001/xml.xsd will change    accordingly; the version at
  • Network accessline 81
    http://www.w3.org/2001/xml.xsd will change
  • Network accessline 82
    accordingly; the version at    http://www.w3.org/2001/03/xml.xsd will not change.   </xs:documentation>
  • Network accessline 83
    http://www.w3.org/2001/03/xml.xsd will not change.
  • Network accessline 103
    <xs:attribute name="base" type="xs:anyURI">   <xs:annotation>    <xs:documentation>See http://www.w3.org/TR/xmlbase/ for
  • Network accessline 104
    <xs:annotation>    <xs:documentation>See http://www.w3.org/TR/xmlbase/ for                      information about this attribute.</xs:documentation>
  • Network accessline 105
    <xs:documentation>See http://www.w3.org/TR/xmlbase/ for

skills/productivity/powerpoint/scripts/office/schemas/ecma/fourth-edition/opc-contentTypes.xsd

  • Network accessline 1
    <U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types"
  • Network accessline 2
    <xs:schema xmlns="http://schemas.openxmlformats.org/package/2006/content-types"
  • Network accessline 3
    xmlns:xs="http://www.w3.org/2001/XMLSchema"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/package/2006/content-types"

skills/productivity/powerpoint/scripts/office/schemas/ecma/fourth-edition/opc-coreProperties.xsd

  • Network accessline 1
    <U+FEFF><?xml version="1.0" encoding="UTF-8"?> <xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties"
  • Network accessline 2
    <xs:schema targetNamespace="http://schemas.openxmlformats.org/package/2006/metadata/core-properties"
  • Network accessline 3
    xmlns="http://schemas.openxmlformats.org/package/2006/metadata/core-properties"
  • Network accessline 4
    xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dc="http://purl.org/dc/elements/1.1/"
  • Network accessline 5
    xmlns:dcterms="http://purl.org/dc/terms/" elementFormDefault="qualified" blockDefault="#all">
  • Network accessline 7
    <xs:import namespace="http://purl.org/dc/elements/1.1/"
  • Network accessline 8
    schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dc.xsd"/>
  • Network accessline 9
    <xs:import namespace="http://purl.org/dc/terms/"
  • Network accessline 10
    schemaLocation="http://dublincore.org/schemas/xmls/qdc/2003/04/02/dcterms.xsd"/>
  • Network accessline 11
    <xs:import id="xml" namespace="http://www.w3.org/XML/1998/namespace"/>

skills/productivity/powerpoint/scripts/office/schemas/ecma/fourth-edition/opc-digSig.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature"
  • Network accessline 2
    <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/digital-signature"
  • Network accessline 3
    xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/package/2006/digital-signature"

skills/productivity/powerpoint/scripts/office/schemas/ecma/fourth-edition/opc-relationships.xsd

  • Network accessline 1
    <U+FEFF><?xml version="1.0" encoding="UTF-8" standalone="no"?> <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships"
  • Network accessline 2
    <xsd:schema xmlns="http://schemas.openxmlformats.org/package/2006/relationships"
  • Network accessline 3
    xmlns:xsd="http://www.w3.org/2001/XMLSchema"
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/package/2006/relationships"

skills/productivity/powerpoint/scripts/office/schemas/mce/mc.xsd

  • Network accessline 1
    <?xml version="1.0" encoding="utf-8"?> <xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 2
    <xsd:schema xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 3
    attributeFormDefault="unqualified" elementFormDefault="qualified" 	targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006" 	xmlns:xsd="http://www.w3.org/2001/XMLSchema">
  • Network accessline 4
    targetNamespace="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 5
    xmlns:xsd="http://www.w3.org/2001/XMLSchema">
  • Network accessline 7
    <!--     This XSD is a modified version of the one found at:     https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd
  • Network accessline 9
    https://github.com/plutext/docx4j/blob/master/xsd/mce/markup-compatibility-2006-MINIMAL.xsd
  • Network accessline 15
    <w:document 			            xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 16
    xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
  • Network accessline 17
    xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main"

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-2010.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns:r="http://schemas.op
  • Network accessline 2
    <!-- <xsd:import id="rel" namespace="http://schemas.openxmlformats.org/officeDocument/2006/relationships" schemaLocation="orel.xsd"/> -->
  • Network accessline 3
    <xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
  • Network accessline 4
    <!-- <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartbasetypes.xsd"/>
  • Network accessline 5
    <xsd:import namespace="http://schemas.openxmlformats.org/drawingml/2006/main" schemaLocation="oartsplineproperties.xsd"/> -->

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-2012.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" elementFormDefault="qualified" att
  • Network accessline 2
    <xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
  • Network accessline 3
    <xsd:import namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-2018.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr
  • Network accessline 2
    <xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-cex-2018.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w="http://schemas.openxmlformats.org/wordprocessingml/2006/main" xmlns:s="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" xmlns:w16="http://schemas.microsoft.
  • Network accessline 2
    <xsd:import id="w16" namespace="http://schemas.microsoft.com/office/word/2018/wordml" schemaLocation="wml-2018.xsd"/>
  • Network accessline 3
    <xsd:import id="w" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>
  • Network accessline 4
    <xsd:import id="s" namespace="http://schemas.openxmlformats.org/officeDocument/2006/sharedTypes" schemaLocation="../ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-cid-2016.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr
  • Network accessline 2
    <xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr
  • Network accessline 2
    <xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>

skills/productivity/powerpoint/scripts/office/schemas/microsoft/wml-symex-2015.xsd

  • Network accessline 1
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:w12="http://schemas.openxmlformats.org/wordprocessingml/2006/main" elementFormDefault="qualified" attributeFormDefault="qualified" blockDefault="#all" xmlns="http://schemas.micr
  • Network accessline 2
    <xsd:import id="w12" namespace="http://schemas.openxmlformats.org/wordprocessingml/2006/main" schemaLocation="../ISO-IEC29500-4_2016/wml.xsd"/>

skills/research/arxiv/SKILL.md

  • Network accessline 19
    | Action | Command | |--------|---------| | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` |
  • Network accessline 20
    |--------|---------| | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | | Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` |
  • Network accessline 21
    | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` |
  • Network accessline 22
    | Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` |
  • Network accessline 23
    | Read abstract (web) | `web_extract(urls=["https://arxiv.org/abs/2402.03300"])` |
  • Network accessline 24
    | Read full paper (PDF) | `web_extract(urls=["https://arxiv.org/pdf/2402.03300"])` |
  • Network accessline 33
    curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5"
  • Dangerous shell operationline 39
    curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5&sortBy=submittedDate&sortOrder=descending" | python3 -c "
  • Network accessline 39
    curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5&sortBy=submittedDate&sortOrder=descending" | python3 -c "
  • Network accessline 40
    import sys, xml.etree.ElementTree as ET ns = {'a': 'http://www.w3.org/2005/Atom'} root = ET.parse(sys.stdin).getroot()
  • Network accessline 41
    ns = {'a': 'http://www.w3.org/2005/Atom'}
  • Network accessline 52
    print(f'   Published: {published} | Categories: {cats}')     print(f'   Abstract: {summary}...')     print(f'   PDF: https://arxiv.org/pdf/{arxiv_id}')
  • Network accessline 53
    print(f'   Abstract: {summary}...')     print(f'   PDF: https://arxiv.org/pdf/{arxiv_id}')     print()
  • Network accessline 54
    print(f'   PDF: https://arxiv.org/pdf/{arxiv_id}')
  • Network accessline 99
    # Latest 10 papers in cs.AI curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI&sortBy=submittedDate&sortOrder=descending&max_results=10"
  • Network accessline 100
    curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI&sortBy=submittedDate&sortOrder=descending&max_results=10"
  • Network accessline 106
    # By arXiv ID curl -s "https://export.arxiv.org/api/query?id_list=2402.03300"
  • Network accessline 107
    curl -s "https://export.arxiv.org/api/query?id_list=2402.03300"
  • Network accessline 109
    # Multiple papers curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001"
  • Network accessline 110
    curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001"
  • Dangerous shell operationline 119
    curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python3 -c "
  • Network accessline 119
    curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python3 -c "
  • Network accessline 120
    import sys, xml.etree.ElementTree as ET ns = {'a': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} root = ET.parse(sys.stdin).getroot()
  • Network accessline 121
    ns = {'a': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'}
  • Network accessline 137
    print(f'  archivePrefix = {{arXiv}},') print(f'  primaryClass  = {{{primary}}},') print(f'  url       = {{https://arxiv.org/abs/{raw_id}}}')
  • Network accessline 138
    print(f'  primaryClass  = {{{primary}}},') print(f'  url       = {{https://arxiv.org/abs/{raw_id}}}') print('}')
  • Network accessline 139
    print(f'  url       = {{https://arxiv.org/abs/{raw_id}}}')
  • Network accessline 150
    # Abstract page (fast, metadata + abstract) web_extract(urls=["https://arxiv.org/abs/2402.03300"])
  • Network accessline 151
    web_extract(urls=["https://arxiv.org/abs/2402.03300"])
  • Network accessline 153
    # Full paper (PDF → markdown via Firecrawl) web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
  • Network accessline 154
    web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
  • Network accessline 172
    Full list: https://arxiv.org/category_taxonomy
  • Network accessline 198
    # By arXiv ID curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python3 -m json.tool
  • Dangerous shell operationline 199
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python3 -m json.tool
  • Network accessline 199
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300?fields=title,authors,citationCount,referenceCount,influentialCitationCount,year,abstract" | python3 -m json.tool
  • Network accessline 201
    # By Semantic Scholar paper ID or DOI curl -s "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example?fields=title,citationCount"
  • Network accessline 202
    curl -s "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example?fields=title,citationCount"
  • Dangerous shell operationline 208
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/citations?fields=title,authors,year,citationCount&limit=10" | python3 -m json.tool
  • Network accessline 208
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/citations?fields=title,authors,year,citationCount&limit=10" | python3 -m json.tool
  • Dangerous shell operationline 214
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/references?fields=title,authors,year,citationCount&limit=10" | python3 -m json.tool
  • Network accessline 214
    curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:2402.03300/references?fields=title,authors,year,citationCount&limit=10" | python3 -m json.tool
  • Dangerous shell operationline 220
    curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=GRPO+reinforcement+learning&limit=5&fields=title,authors,year,citationCount,externalIds" | python3 -m json.tool
  • Network accessline 220
    curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=GRPO+reinforcement+learning&limit=5&fields=title,authors,year,citationCount,externalIds" | python3 -m json.tool
  • Dangerous shell operationline 226
    curl -s -X POST "https://api.semanticscholar.org/recommendations/v1/papers/"    -H "Content-Type: application/json"    -d '{"positivePaperIds": ["arXiv:2402.03300"], "negativePaperIds": []}' | python3 -m json.tool
  • Network accessline 226
    curl -s -X POST "https://api.semanticscholar.org/recommendations/v1/papers/" \
  • Dangerous shell operationline 234
    curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python3 -m json.tool
  • Network accessline 234
    curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python3 -m json.tool
  • Network accessline 245
    1. **Discover**: `python scripts/search_arxiv.py "your topic" --sort date --max 10` 2. **Assess impact**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID?fields=citationCount,influentialCitationCount"`
  • Network accessline 246
    2. **Assess impact**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID?fields=citationCount,influentialCitationCount"`
  • Network accessline 247
    3. **Read abstract**: `web_extract(urls=["https://arxiv.org/abs/ID"])` 4. **Read full paper**: `web_extract(urls=["https://arxiv.org/pdf/ID"])` 5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/refe
  • Network accessline 248
    4. **Read full paper**: `web_extract(urls=["https://arxiv.org/pdf/ID"])` 5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/references?fields=title,citationCount&limit=20"` 6. **Get recommendations**
  • Network accessline 249
    5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/references?fields=title,citationCount&limit=20"`
  • Network accessline 251
    7. **Track authors**: `curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=NAME"`
  • Network accessline 263
    - Semantic Scholar returns JSON — pipe through `python3 -m json.tool` for readability - arXiv IDs: old format (`hep-th/0601001`) vs new (`2402.03300`) - PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}`
  • Network accessline 264
    - arXiv IDs: old format (`hep-th/0601001`) vs new (`2402.03300`) - PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}` - HTML (when available): `https://arxiv.org/html/{id}`
  • Network accessline 265
    - PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}`
  • Network accessline 266
    - HTML (when available): `https://arxiv.org/html/{id}`
  • Network accessline 272
    - `arxiv.org/abs/1706.03762v1` points to a **specific** immutable version - When generating citations, preserve the version suffix you actually read to prevent citation drift (a later version may substantially change content) - The API `<id
  • Network accessline 274
    - The API `<id>` field returns the versioned URL (e.g., `http://arxiv.org/abs/1706.03762v7`)

skills/research/arxiv/scripts/search_arxiv.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 18
    NS = {'a': 'http://www.w3.org/2005/Atom'}
  • Network accessline 44
    url = "https://export.arxiv.org/api/query?" + "&".join(f"{k}={v}" for k, v in params.items())
  • Network accessline 46
    req = urllib.request.Request(url, headers={'User-Agent': 'GaussAgent/1.0'})
  • Network accessline 47
    with urllib.request.urlopen(req, timeout=15) as resp:
  • Network accessline 57
    total = root.find('{http://a9.com/-/spec/opensearch/1.1/}totalResults')
  • Network accessline 76
    print(f"   Categories: {cats}")         print(f"   Abstract: {summary[:300]}{'...' if len(summary) > 300 else ''}")         print(f"   Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}")
  • Network accessline 77
    print(f"   Abstract: {summary[:300]}{'...' if len(summary) > 300 else ''}")         print(f"   Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}")         print()
  • Network accessline 78
    print(f"   Links: https://arxiv.org/abs/{arxiv_id} | https://arxiv.org/pdf/{arxiv_id}")

skills/research/blogwatcher/SKILL.md

  • Network accessline 8
    gauss:     tags: [RSS, Blogs, Feed-Reader, Monitoring]     homepage: https://github.com/Hyaxia/blogwatcher
  • Network accessline 9
    tags: [RSS, Blogs, Feed-Reader, Monitoring]     homepage: https://github.com/Hyaxia/blogwatcher prerequisites:
  • Network accessline 10
    homepage: https://github.com/Hyaxia/blogwatcher
  • Network accessline 26
    - Add a blog: `blogwatcher add "My Blog" https://example.com`
  • Network accessline 40
    xkcd     URL: https://xkcd.com
  • Network accessline 41
    URL: https://xkcd.com

skills/research/domain-intel/SKILL.md

  • Network accessline 52
    - **Use `web_search`** for general research about what a domain/company does - **Use `web_extract`** to get the actual content of a webpage - **Use `terminal` with `curl -I`** for a simple "is this URL reachable" check
  • Network accessline 54
    - **Use `terminal` with `curl -I`** for a simple "is this URL reachable" check
  • Network accessline 59
    | "Find info about a company" | `web_search` | General research, not domain-specific | | "Is this website safe?" | `web_search` | Reputation checks need web context | | "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP
  • Network accessline 60
    | "Is this website safe?" | `web_search` | Reputation checks need web context | | "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP check | | "Find subdomains of X" | **This skill** | Only passive source for this |
  • Network accessline 61
    | "Check if a URL is reachable" | `terminal` with `curl -I` | Simple HTTP check |
  • Network accessline 96
    *Contributed by [@FurkanL0](https://github.com/FurkanL0)*

skills/research/domain-intel/scripts/domain_intel.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 32
    url = f"https://crt.sh/?q=%25.{urllib.parse.quote(domain)}&output=json"
  • Network accessline 33
    req = urllib.request.Request(url, headers={
  • Network accessline 34
    "User-Agent": "domain-intel-skill/1.0", "Accept": "application/json",     })     with urllib.request.urlopen(req, timeout=15) as r:
  • Network accessline 35
    })     with urllib.request.urlopen(req, timeout=15) as r:         entries = json.loads(r.read().decode())
  • Network accessline 36
    with urllib.request.urlopen(req, timeout=15) as r:
  • Network accessline 85
    try:         ctx = ssl.create_default_context()         with socket.create_connection((host, port), timeout=timeout) as sock:
  • Network accessline 86
    ctx = ssl.create_default_context()         with socket.create_connection((host, port), timeout=timeout) as sock:             with ctx.wrap_socket(sock, server_hostname=host) as s:
  • Network accessline 87
    with socket.create_connection((host, port), timeout=timeout) as sock:
  • Network accessline 93
    ctx.check_hostname = False         ctx.verify_mode = ssl.CERT_NONE         with socket.create_connection((host, port), timeout=timeout) as sock:
  • Network accessline 94
    ctx.verify_mode = ssl.CERT_NONE         with socket.create_connection((host, port), timeout=timeout) as sock:             with ctx.wrap_socket(sock, server_hostname=host) as s:
  • Network accessline 95
    with socket.create_connection((host, port), timeout=timeout) as sock:
  • Network accessline 162
    try:         with socket.create_connection((server, 43), timeout=10) as s:
  • Network accessline 163
    with socket.create_connection((server, 43), timeout=10) as s:
  • Network accessline 231
    records["AAAA"] = []         else:             url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}"
  • Network accessline 232
    else:             url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}"             try:
  • Network accessline 233
    url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type={qtype}"
  • Network accessline 234
    try:                 req = urllib.request.Request(url, headers={"User-Agent": "domain-intel-skill/1.0"})                 with urllib.request.urlopen(req, timeout=10) as r:
  • Network accessline 235
    req = urllib.request.Request(url, headers={"User-Agent": "domain-intel-skill/1.0"})
  • Network accessline 236
    with urllib.request.urlopen(req, timeout=10) as r:
  • Network accessline 260
    try:         ns_url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type=NS"
  • Network accessline 261
    ns_url = f"https://dns.google/resolve?name={urllib.parse.quote(domain)}&type=NS"
  • Network accessline 262
    req = urllib.request.Request(ns_url, headers={"User-Agent": "domain-intel-skill/1.0"})
  • Network accessline 263
    with urllib.request.urlopen(req, timeout=10) as r:
  • Network accessline 276
    ctx.check_hostname = False         ctx.verify_mode = ssl.CERT_NONE         with socket.create_connection((domain, 443), timeout=3) as s:
  • Network accessline 277
    ctx.verify_mode = ssl.CERT_NONE         with socket.create_connection((domain, 443), timeout=3) as s:             with ctx.wrap_socket(s, server_hostname=domain):
  • Network accessline 278
    with socket.create_connection((domain, 443), timeout=3) as s:
  • Network accessline 290
    if server:         try:             with socket.create_connection((server, 43), timeout=10) as s:
  • Network accessline 291
    try:             with socket.create_connection((server, 43), timeout=10) as s:                 s.sendall((domain + "\r\n").encode())
  • Network accessline 292
    with socket.create_connection((server, 43), timeout=10) as s:

skills/research/duckduckgo-search/SKILL.md

  • Access to secretsline 20
    [Credential-related source omitted]
  • Network accessline 173
    - **Rate limiting**: DuckDuckGo may throttle after many rapid requests. Add a short delay between searches if needed. - **No content extraction**: ddgs returns snippets, not full page content. Use `web_extract` or curl for that.
  • Network accessline 174
    - **No content extraction**: ddgs returns snippets, not full page content. Use `web_extract` or curl for that.

skills/research/duckduckgo-search/scripts/duckduckgo.sh

  • Executable scriptline 1
    #!/bin/bash

skills/research/ml-paper-writing/SKILL.md

  • Network accessline 67
    claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"
  • Network accessline 74
    "exa": {       "type": "http",       "url": "https://mcp.exa.ai/mcp"
  • Network accessline 75
    "type": "http",       "url": "https://mcp.exa.ai/mcp"     }
  • Network accessline 76
    "url": "https://mcp.exa.ai/mcp"
  • Network accessline 361
    | Source | Key Contribution | Link | |--------|-----------------|------| | **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLp
  • Network accessline 362
    |--------|-----------------|------| | **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-
  • Network accessline 363
    | **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) |
  • Network accessline 364
    | **Sebastian Farquhar** (DeepMind) | 5-sentence abstract formula | [How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) |
  • Network accessline 365
    | **Gopen & Swan** | 7 principles of reader expectations | [Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) |
  • Network accessline 366
    | **Zachary Lipton** | Word choice, eliminating hedging | [Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) |
  • Network accessline 367
    | **Jacob Steinhardt** (UC Berkeley) | Precision, consistent terminology | [Writing Tips](https://bounded-regret.ghost.io/) |
  • Network accessline 368
    | **Ethan Perez** (Anthropic) | Micro-level clarity tips | [Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) |
  • Network accessline 766
    def doi_to_bibtex(doi: str) -> str:     """Get verified BibTeX from DOI via CrossRef."""     response = requests.get(
  • Network accessline 767
    """Get verified BibTeX from DOI via CrossRef."""     response = requests.get(         f"https://doi.org/{doi}",
  • Network accessline 768
    response = requests.get(
  • Network accessline 769
    f"https://doi.org/{doi}",
  • Network accessline 922
    - **VS Code/Cursor**: Install LaTeX Workshop extension + TeX Live → Save to auto-compile - **Command line**: `latexmk -pdf main.tex` or `pdflatex` + `bibtex` workflow - **Online**: Upload to [Overleaf](https://overleaf.com)
  • Network accessline 924
    - **Online**: Upload to [Overleaf](https://overleaf.com)
  • Network accessline 930
    **Writing Philosophy:** - [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Narrative, "What/Why/So What"
  • Network accessline 931
    - [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Narrative, "What/Why/So What"
  • Network accessline 932
    - [Farquhar: How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) - 5-sentence abstract
  • Network accessline 933
    - [Gopen & Swan: Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) - 7 reader expectation principles
  • Network accessline 934
    - [Lipton: Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) - Word choice
  • Network accessline 935
    - [Perez: Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) - Micro-level clarity
  • Network accessline 937
    **APIs:** [Semantic Scholar](https://api.semanticscholar.org/api-docs/) | [CrossRef](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | [arXiv](https://info.arxiv.org/help/api/basics.html)
  • Network accessline 939
    **Venues:** [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | [ICML](https://icml.cc/Conferences/2025/AuthorInstructions) | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | [ACL](https://github.com/acl-org/a

skills/research/ml-paper-writing/references/citation-workflow.md

  • Network accessline 124
    # Check CrossRef (via DOI)     if doi:         resp = requests.get(f"https://api.crossref.org/works/{doi}")
  • Network accessline 125
    if doi:         resp = requests.get(f"https://api.crossref.org/works/{doi}")         if resp.status_code == 200:
  • Network accessline 126
    resp = requests.get(f"https://api.crossref.org/works/{doi}")
  • Network accessline 130
    # Check arXiv     if arxiv_id:         resp = requests.get(
  • Network accessline 131
    if arxiv_id:         resp = requests.get(             f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
  • Network accessline 132
    resp = requests.get(
  • Network accessline 133
    f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
  • Network accessline 148
    def doi_to_bibtex(doi: str) -> str:     """Get verified BibTeX from DOI via CrossRef content negotiation."""     response = requests.get(
  • Network accessline 149
    """Get verified BibTeX from DOI via CrossRef content negotiation."""     response = requests.get(         f"https://doi.org/{doi}",
  • Network accessline 150
    response = requests.get(
  • Network accessline 151
    f"https://doi.org/{doi}",
  • Access to secretsline 249
    [Credential-related source omitted]
  • Access to secretsline 250
    [Credential-related source omitted]
  • Network accessline 284
    if paper.doi:             try:                 resp = requests.get(
  • Network accessline 285
    try:                 resp = requests.get(                     f"https://api.crossref.org/works/{paper.doi}",
  • Network accessline 286
    resp = requests.get(
  • Network accessline 287
    f"https://api.crossref.org/works/{paper.doi}",
  • Network accessline 296
    if paper.arxiv_id:             try:                 resp = requests.get(
  • Network accessline 297
    try:                 resp = requests.get(                     f"http://export.arxiv.org/api/query?id_list={paper.arxiv_id}",
  • Network accessline 298
    resp = requests.get(
  • Network accessline 299
    f"http://export.arxiv.org/api/query?id_list={paper.arxiv_id}",
  • Network accessline 311
    if paper.doi:             try:                 resp = requests.get(
  • Network accessline 312
    try:                 resp = requests.get(                     f"https://doi.org/{paper.doi}",
  • Network accessline 313
    resp = requests.get(
  • Network accessline 314
    f"https://doi.org/{paper.doi}",
  • Network accessline 551
    **APIs:** - Semantic Scholar: https://api.semanticscholar.org/api-docs/
  • Network accessline 552
    - Semantic Scholar: https://api.semanticscholar.org/api-docs/
  • Network accessline 553
    - CrossRef: https://www.crossref.org/documentation/retrieve-metadata/rest-api/
  • Network accessline 554
    - arXiv: https://info.arxiv.org/help/api/basics.html
  • Network accessline 555
    - OpenAlex: https://docs.openalex.org/
  • Network accessline 557
    **Python Libraries:** - `semanticscholar`: https://pypi.org/project/semanticscholar/
  • Network accessline 558
    - `semanticscholar`: https://pypi.org/project/semanticscholar/
  • Network accessline 559
    - `arxiv`: https://pypi.org/project/arxiv/
  • Network accessline 560
    - `habanero` (CrossRef): https://github.com/sckott/habanero
  • Network accessline 562
    **Verification Tools:** - Citely: https://citely.ai/citation-checker
  • Network accessline 563
    - Citely: https://citely.ai/citation-checker
  • Network accessline 564
    - ReciteWorks: https://reciteworks.com/

skills/research/ml-paper-writing/references/sources.md

  • Network accessline 11
    | Source | Author | URL | Key Contribution | |--------|--------|-----|------------------| | **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/high
  • Network accessline 12
    |--------|--------|-----|------------------| | **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-pape
  • Network accessline 13
    | **Highly Opinionated Advice on How to Write ML Papers** | Neel Nanda | [Alignment Forum](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) | Narrative framework, "What/Why/So What"
  • Network accessline 14
    | **How to Write ML Papers** | Sebastian Farquhar (DeepMind) | [Blog](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) | 5-sentence abstract formula, structure templates |
  • Network accessline 15
    | **A Survival Guide to a PhD** | Andrej Karpathy | [Blog](http://karpathy.github.io/2016/09/07/phd/) | Paper structure recipe, contribution framing |
  • Network accessline 16
    | **Heuristics for Scientific Writing** | Zachary Lipton (CMU) | [Blog](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) | Word choice, section balance, intensifier warni
  • Network accessline 17
    | **Advice for Authors** | Jacob Steinhardt (UC Berkeley) | [Blog](https://jsteinhardt.stat.berkeley.edu/blog/advice-for-authors) | Precision over brevity, consistent terminology |
  • Network accessline 18
    | **Easy Paper Writing Tips** | Ethan Perez (Anthropic) | [Blog](https://ethanperez.net/easy-paper-writing-tips/) | Micro-level tips, apostrophe unfolding, clarity tricks |
  • Network accessline 22
    | Source | Author | URL | Key Contribution | |--------|--------|-----|------------------| | **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress position
  • Network accessline 23
    |--------|--------|-----|------------------| | **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress positions, old-before-new, 7 principles | | **Summary
  • Network accessline 24
    | **The Science of Scientific Writing** | Gopen & Swan | [PDF](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) | Topic/stress positions, old-before-new, 7 principles |
  • Network accessline 25
    | **Summary of Science of Scientific Writing** | Lawrence Crowl | [Summary](https://www.crowl.org/Lawrence/writing/GopenSwan90.html) | Condensed version of Gopen & Swan |
  • Network accessline 29
    | Source | URL | Key Contribution | |--------|-----|------------------| | How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) |
  • Network accessline 30
    |--------|-----|------------------| | How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) | Practical walkthrough, LaTeX tips |
  • Network accessline 31
    | How To Write A Research Paper In ML | [Blog](https://grigorisg9gr.github.io/machine%20learning/research%20paper/how-to-write-a-research-paper-in-machine-learning/) | Practical walkthrough, LaTeX tips |
  • Network accessline 32
    | A Recipe for Training Neural Networks | [Karpathy Blog](http://karpathy.github.io/2019/04/25/recipe/) | Debugging methodology that translates to paper structure |
  • Network accessline 33
    | ICML Paper Writing Best Practices | [ICML](https://icml.cc/Conferences/2022/BestPractices) | Official venue guidance |
  • Network accessline 34
    | Bill Freeman's Writing Slides | [MIT](https://billf.mit.edu/sites/default/files/documents/cvprPapers.pdf) | Visual guide to paper structure |
  • Network accessline 42
    | Document | URL | Purpose | |----------|-----|---------| | Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist |
  • Network accessline 43
    |----------|-----|---------| | Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist | | Reviewer Guidelines 2025 | [NeurIPS](https://neurips.cc/Conferences/2025/ReviewerGuidel
  • Network accessline 44
    | Paper Checklist Guidelines | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | 16-item mandatory checklist |
  • Network accessline 45
    | Reviewer Guidelines 2025 | [NeurIPS](https://neurips.cc/Conferences/2025/ReviewerGuidelines) | Evaluation criteria, scoring |
  • Network accessline 46
    | Style Files | [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | LaTeX templates |
  • Network accessline 50
    | Document | URL | Purpose | |----------|-----|---------| | Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements |
  • Network accessline 51
    |----------|-----|---------| | Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements | | Reviewer Instructions 2025 | [ICML](https://icml.cc/Conferences/2025/ReviewerInstructions) | Review for
  • Network accessline 52
    | Paper Guidelines | [ICML](https://icml.cc/Conferences/2024/PaperGuidelines) | Submission requirements |
  • Network accessline 53
    | Reviewer Instructions 2025 | [ICML](https://icml.cc/Conferences/2025/ReviewerInstructions) | Review form, evaluation |
  • Network accessline 54
    | Style & Author Instructions | [ICML](https://icml.cc/Conferences/2022/StyleAuthorInstructions) | Formatting specifications |
  • Network accessline 58
    | Document | URL | Purpose | |----------|-----|---------| | Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure |
  • Network accessline 59
    |----------|-----|---------| | Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure | | Reviewer Guide 2025 | [ICLR](https://iclr.cc/Conferences/2025/ReviewerGuide) | Review proc
  • Network accessline 60
    | Author Guide 2026 | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | Submission requirements, LLM disclosure |
  • Network accessline 61
    | Reviewer Guide 2025 | [ICLR](https://iclr.cc/Conferences/2025/ReviewerGuide) | Review process, evaluation |
  • Network accessline 65
    | Document | URL | Purpose | |----------|-----|---------| | ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates |
  • Network accessline 66
    |----------|-----|---------| | ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates | | ACL Rolling Review | [ARR](https://aclrollingreview.org/) | Submission process |
  • Network accessline 67
    | ACL Style Files | [GitHub](https://github.com/acl-org/acl-style-files) | LaTeX templates |
  • Network accessline 68
    | ACL Rolling Review | [ARR](https://aclrollingreview.org/) | Submission process |
  • Network accessline 72
    | Document | URL | Purpose | |----------|-----|---------| | Author Kit 2026 | [AAAI](https://aaai.org/authorkit26/) | Templates and guidelines |
  • Network accessline 74
    | Author Kit 2026 | [AAAI](https://aaai.org/authorkit26/) | Templates and guidelines |
  • Network accessline 78
    | Document | URL | Purpose | |----------|-----|---------| | Template | [GitHub](https://github.com/COLM-org/Template) | LaTeX templates |
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    | Template | [GitHub](https://github.com/COLM-org/Template) | LaTeX templates |
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    | API | Documentation | Best For | |-----|---------------|----------| | **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs |
  • Network accessline 89
    |-----|---------------|----------| | **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs | | **CrossRef** | [Docs](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | DO
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    | **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs |
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    | **CrossRef** | [Docs](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | DOI lookup, BibTeX retrieval |
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    | **arXiv** | [Docs](https://info.arxiv.org/help/api/basics.html) | Preprints, PDF access |
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    | **OpenAlex** | [Docs](https://docs.openalex.org/) | Open alternative, bulk access |
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    | Tool | URL | Purpose | |------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification |
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    |------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | | ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking |
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    | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification |
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    | ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking |
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    | Tool | URL | Purpose | |------|-----|---------| | PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams |
  • Network accessline 117
    |------|-----|---------| | PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams | | SciencePlots | [GitHub](https://github.com/garrettj403/SciencePlots) | Publication-ready matplotlib |
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    | PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams |
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    | SciencePlots | [GitHub](https://github.com/garrettj403/SciencePlots) | Publication-ready matplotlib |
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    | Okabe-Ito Palette | [Reference](https://jfly.uni-koeln.de/color/) | Colorblind-safe colors |
  • Network accessline 124
    | Resource | URL | Purpose | |----------|-----|---------| | Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor |
  • Network accessline 125
    |----------|-----|---------| | Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor | | BibLaTeX Guide | [CTAN](https://ctan.org/pkg/biblatex) | Modern citation management |
  • Network accessline 126
    | Overleaf Templates | [Overleaf](https://www.overleaf.com/latex/templates) | Online LaTeX editor |
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    | BibLaTeX Guide | [CTAN](https://ctan.org/pkg/biblatex) | Modern citation management |
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    | Source | URL | Key Finding | |--------|-----|-------------| | AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate |
  • Network accessline 134
    |--------|-----|-------------| | AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate | | Hallucination in AI Writing | [PMC](https://pmc.ncbi.nlm.nih.gov/articles/P
  • Network accessline 135
    | AI Hallucinations in Citations | [Enago](https://www.enago.com/academy/ai-hallucinations-research-citations/) | ~40% error rate |
  • Network accessline 136
    | Hallucination in AI Writing | [PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC10726751/) | Types of citation errors |
  • Network accessline 137
    | NeurIPS 2025 AI Report | [ByteIota](https://byteiota.com/neurips-2025-100-ai-hallucinations-slip-through-review/) | 100+ hallucinated citations |

skills/research/ml-paper-writing/templates/README.md

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    **Setup:** 1. Install [TeX Live](https://www.tug.org/texlive/) (full distribution recommended)
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    1. Install [TeX Live](https://www.tug.org/texlive/) (full distribution recommended)
  • Network accessline 13
    - macOS: `brew install --cask mactex`    - Ubuntu: `sudo apt install texlive-full`    - Windows: Download from [tug.org/texlive](https://www.tug.org/texlive/)
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    - Ubuntu: `sudo apt install texlive-full`
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    - Windows: Download from [tug.org/texlive](https://www.tug.org/texlive/)
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    1. Go to [overleaf.com](https://www.overleaf.com)
  • Network accessline 108
    | Conference | Directory | Year | Source | |------------|-----------|------|--------| | ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) |
  • Network accessline 109
    |------------|-----------|------|--------| | ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) | | ICLR | `iclr2026/` | 2026 | [Official GitHub](https://github.com/ICLR/Master-Template) |
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    | ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) |
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    | ICLR | `iclr2026/` | 2026 | [Official GitHub](https://github.com/ICLR/Master-Template) |
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    | NeurIPS | `neurips2025/` | 2025 | Community template | | ACL | `acl/` | 2025+ | [Official ACL](https://github.com/acl-org/acl-style-files) | | AAAI | `aaai2026/` | 2026 | [AAAI Author Kit](https://aaai.org/authorkit26/) |
  • Network accessline 113
    | ACL | `acl/` | 2025+ | [Official ACL](https://github.com/acl-org/acl-style-files) |
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    | AAAI | `aaai2026/` | 2026 | [AAAI Author Kit](https://aaai.org/authorkit26/) |
  • Network accessline 115
    | COLM | `colm2025/` | 2025 | [Official COLM](https://github.com/COLM-org/Template) |
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    - ICML: https://icml.cc/
  • Network accessline 247
    - ICLR: https://iclr.cc/
  • Network accessline 248
    - NeurIPS: https://neurips.cc/
  • Network accessline 249
    - ACL: https://github.com/acl-org/acl-style-files
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    - AAAI: https://aaai.org/
  • Network accessline 251
    - COLM: https://colmweb.org/

skills/research/ml-paper-writing/templates/aaai2026/README.md

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    **📖 在线阅读和测试模板**: [https://cn.overleaf.com/read/wyhcnvcrtpyt#cd4a07](https://cn.overleaf.com/read/wyhcnvcrtpyt#cd4a07)
  • Network accessline 15
    **📖 Online View and Test Template**: [https://cn.overleaf.com/read/wyhcnvcrtpyt#cd4a07](https://cn.overleaf.com/read/wyhcnvcrtpyt#cd4a07)

skills/research/ml-paper-writing/templates/aaai2026/aaai2026-unified-supp.tex

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    Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\
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    \textsuperscript{\rm 1}Association for the Advancement of Artificial Intelligence\\

skills/research/ml-paper-writing/templates/aaai2026/aaai2026-unified-template.tex

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    Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\
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    \textsuperscript{\rm 1}Association for the Advancement of Artificial Intelligence\\
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    % Sunil Issar\textsuperscript{\rm 2},
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    % J. Scott Penberthy\textsuperscript{\rm 3},
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    % George Ferguson\textsuperscript{\rm 4},
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    % Hans Guesgen\textsuperscript{\rm 5}
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    First Author Name\textsuperscript{\rm 1},
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    Second Author Name\textsuperscript{\rm 2},
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    Third Author Name\textsuperscript{\rm 1}
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    \textsuperscript{\rm 1}Affiliation 1\\
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    \textsuperscript{\rm 2}Affiliation 2\\
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    % NOTE: For anonymous submissions, do not include links that could reveal your identity % \begin{links} %     \link{Code}{https://aaai.org/example/code}
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    % \begin{links} %     \link{Code}{https://aaai.org/example/code} %     \link{Datasets}{https://aaai.org/example/datasets}
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    %     \link{Code}{https://aaai.org/example/code}
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    %     \link{Datasets}{https://aaai.org/example/datasets}
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    %     \link{Extended version}{https://aaai.org/example/extended-version}
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    % You must keep this block between (not within) the abstract and the main body of the paper. \begin{links}     \link{Code}{https://aaai.org/example/code}
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    \begin{links}     \link{Code}{https://aaai.org/example/code}     \link{Datasets}{https://aaai.org/example/datasets}
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    \link{Code}{https://aaai.org/example/code}
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    \link{Datasets}{https://aaai.org/example/datasets}
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    \link{Extended version}{https://aaai.org/example/extended-version}
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    \begin{scriptsize}\begin{verbatim} \begin{links}   \link{Code}{https://aaai.org/example/guidelines}
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    \begin{links}   \link{Code}{https://aaai.org/example/guidelines}   \link{Datasets}{https://aaai.org/example/datasets}
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    \link{Code}{https://aaai.org/example/guidelines}
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    \link{Datasets}{https://aaai.org/example/datasets}
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    \link{Extended version}{https://aaai.org/example}
  • Network accessline 525
    \subsubsection{Obtaining Type 1 Computer Modern for \LaTeX{}.} If you use Computer Modern for the mathematics in your paper (you cannot use it for the text) you may need to download type 1 Computer fonts. They are available without charge f
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    http://www.ams.org/tex/type1-fonts.html.
  • Network accessline 532
    \subsection{Title and Authors} Your title must appear centered over both text columns in sixteen-point bold type (twenty-four point leading). The title must be written in Title Case capitalization rules (not sentence case). The rules are a
  • Network accessline 533
    Your title must appear centered over both text columns in sixteen-point bold type (twenty-four point leading). The title must be written in Title Case capitalization rules (not sentence case). The rules are a bit involved, but in general ve
  • Dangerous shell operationline 551
    \noindent For authors from different institutions, use \textbackslash textsuperscript \{\textbackslash rm x \} to match authors and affiliations. Notice that there should not be any spaces between the author name (or comma following it) and
  • Dangerous shell operationline 555
    AuthorOne\equalcontrib\textsuperscript{\rm 1,\rm 2},
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    AuthorTwo\equalcontrib\textsuperscript{\rm 2},
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    AuthorThree\textsuperscript{\rm 3},\\
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    AuthorFour\textsuperscript{\rm 4},
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    AuthorFive \textsuperscript{\rm 5}}
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    \textsuperscript{\rm 1}AffiliationOne,\\
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    \textsuperscript{\rm 2}AffiliationTwo,\\
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    \textsuperscript{\rm 3}AffiliationThree,\\
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    \textsuperscript{\rm 4}AffiliationFour,\\
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    \textsuperscript{\rm 5}AffiliationFive\\
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    @misc(key,   [...]   howpublished="\url{http://...}",
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    [...]   howpublished="\url{http://...}",   note="Accessed: YYYY-mm-dd",
  • Network accessline 851
    howpublished="\url{http://...}",
  • Network accessline 859
    \vspace{.2em} For the most up to date version of the AAAI reference style, please consult the \textit{AI Magazine} Author Guidelines at \url{https://aaai.org/ojs/index.php/aimagazine/about/submissions#authorGuidelines}
  • Network accessline 860
    For the most up to date version of the AAAI reference style, please consult the \textit{AI Magazine} Author Guidelines at \url{https://aaai.org/ojs/index.php/aimagazine/about/submissions#authorGuidelines}

skills/research/ml-paper-writing/templates/aaai2026/aaai2026.bib

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    publisher = {American Association for the Advancement of Science},   issn = {0036-8075},   URL = {https://science.sciencemag.org/content/208/4447/1019},
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    issn = {0036-8075},   URL = {https://science.sciencemag.org/content/208/4447/1019},   eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf},
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    URL = {https://science.sciencemag.org/content/208/4447/1019},
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    eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf},
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    year = {1984}, issn = {0020-7373}, doi = {https://doi.org/10.1016/S0020-7373(84)80003-6},
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    issn = {0020-7373}, doi = {https://doi.org/10.1016/S0020-7373(84)80003-6}, url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036},
  • Network accessline 60
    doi = {https://doi.org/10.1016/S0020-7373(84)80003-6},
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    url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036},
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    title        = "Pluto: The 'Other' Red Planet",   author       = "{NASA}",   howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",
  • Network accessline 107
    author       = "{NASA}",   howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",   year         = 2015,
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    howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",

skills/research/ml-paper-writing/templates/aaai2026/aaai2026.sty

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    \@ifpackageloaded{bbm}%     {%       \PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with-
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    {%       \PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with-type-1-fonts for possible al
  • Network accessline 243
    \PackageError{aaai}{You must not use bbm package in AAAI papers because it introduces Type 3 fonts which are forbidden.}{See https://tex.stackexchange.com/questions/479160/a-replacement-to-mathbbm1-with-type-1-fonts for possible alternative

skills/research/ml-paper-writing/templates/acl/README.md

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    - as an [Overleaf template](https://www.overleaf.com/latex/templates/association-for-computational-linguistics-acl-conference/jvxskxpnznfj)
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    - as a [.zip file](https://github.com/acl-org/acl-style-files/archive/refs/heads/master.zip)
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    Please see [`acl_latex.tex`](https://github.com/acl-org/acl-style-files/blob/master/acl_latex.tex) for an example.
  • Network accessline 21
    - [Paper formatting guidelines](https://acl-org.github.io/ACLPUB/formatting.html)
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    - git pull from github to a local repository - then, git push from your local repository to overleaf project      - Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549
  • Network accessline 47
    - then, git push from your local repository to overleaf project      - Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549     - Overleaf git url is https://git.overleaf.com/5f64f1fb97c4c50001b60549
  • Network accessline 48
    - Overleaf project is https://www.overleaf.com/project/5f64f1fb97c4c50001b60549
  • Network accessline 49
    - Overleaf git url is https://git.overleaf.com/5f64f1fb97c4c50001b60549

skills/research/ml-paper-writing/templates/acl/acl.sty

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    % The official sources can be found at % %     https://github.com/acl-org/acl-style-files/
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    % %     https://github.com/acl-org/acl-style-files/ %
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    %     https://github.com/acl-org/acl-style-files/
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    \ifacl@linenumbers   % Add draft line numbering via the lineno package   % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/
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    % Add draft line numbering via the lineno package   % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/   \usepackage[switch,mathlines]{lineno}
  • Network accessline 37
    % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/
  • Network accessline 61
    % Patch amsmath commands so that the previous line and the equation itself   % are numbered. Bug: multline has an extra line number.   % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align
  • Network accessline 62
    % are numbered. Bug: multline has an extra line number.   % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align   \usepackage{etoolbox} %% <- for \pretocmd, \apptocmd and \patchcmd
  • Network accessline 63
    % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align

skills/research/ml-paper-writing/templates/acl/acl_latex.tex

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    % For Vietnamese characters % \usepackage[T5]{fontenc} % See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets
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    % See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets
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    These instructions are for authors submitting papers to *ACL conferences using \LaTeX. They are not self-contained. All authors must follow the general instructions for *ACL proceedings,\footnote{\url{http://acl-org.github.io/ACLPUB/formatt
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    You can obtain the complete ACL Anthology as a Bib\TeX{} file from \url{https://aclweb.org/anthology/anthology.bib.gz}.

skills/research/ml-paper-writing/templates/acl/acl_natbib.bst

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    %%% Modification of BibTeX style file acl_natbib_nourl.bst %%% ... by urlbst, version 0.9.1 (marked with "% urlbst") %%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst>
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    %%% ... by urlbst, version 0.9.1 (marked with "% urlbst") %%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst> %%% Modifications Copyright 2002–23, Norman Gray,
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    %%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst>
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    "online" 'onlinestring :=	% label that a resource is online   "[link]" 'linktextstring :=	% anonymous link text   "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl :=	% prefix to make URL from PUBMED
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    "[link]" 'linktextstring :=	% anonymous link text   "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl :=	% prefix to make URL from PUBMED   "https://doi.org/" 'doiurl :=	% prefix to make URL from DOI
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    "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl :=	% prefix to make URL from PUBMED
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    "https://doi.org/" 'doiurl :=	% prefix to make URL from DOI
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    "doi:" 'doiprefix :=	% printed text to introduce DOI   "https://arxiv.org/abs/" 'eprinturl :=	% prefix to make URL from eprint ref   "cited " 'citedstring :=	% label in "lastchecked" remark
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    "https://arxiv.org/abs/" 'eprinturl :=	% prefix to make URL from eprint ref
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    { doi empty$     { "" }     { doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack
  • Network accessline 1192
    { "" }     { doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack       's := 't :=
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    { doi parse.doi % leaves "https://doi.org/DOI" DOI on the stack
  • Network accessline 1231
    % then don't print the URL (thanks to Joseph Wright       % for (the original version of) this code,       % at http://tex.stackexchange.com/questions/5660)
  • Network accessline 1232
    % for (the original version of) this code,       % at http://tex.stackexchange.com/questions/5660)       adddoi
  • Network accessline 1233
    % at http://tex.stackexchange.com/questions/5660)
  • Network accessline 1287
    % author, note, year, month, and lastchecked fields optional % See references %   ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm
  • Network accessline 1288
    % See references %   ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm %   http://www.classroom.net/classroom/CitingNetResources.html
  • Network accessline 1289
    %   ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm
  • Network accessline 1290
    %   http://www.classroom.net/classroom/CitingNetResources.html
  • Network accessline 1291
    %   http://neal.ctstateu.edu/history/cite.html
  • Network accessline 1292
    %   http://www.cas.usf.edu/english/walker/mla.html

skills/research/ml-paper-writing/templates/acl/anthology.bib.txt

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    For example, https://aclanthology.org/N04-1035 can be cited as \cite{galley-etal-2004-whats}.
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    The consolidated file can be downloaded from here: - https://aclanthology.org/anthology.bib
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    - https://aclanthology.org/anthology.bib
  • Network accessline 19
    There are currently (2025) two files: - https://aclanthology.org/anthology-1.bib
  • Network accessline 20
    - https://aclanthology.org/anthology-1.bib
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    - https://aclanthology.org/anthology-2.bib

skills/research/ml-paper-writing/templates/acl/custom.bib

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    volume    = {arXiv:1503.06733},     year      = {2015},     url       = {http://arxiv.org/abs/1503.06733},
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    year      = {2015},     url       = {http://arxiv.org/abs/1503.06733},     note    = {version 2}
  • Network accessline 53
    url       = {http://arxiv.org/abs/1503.06733},

skills/research/ml-paper-writing/templates/acl/formatting.md

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    > https://acl-org.github.io/ACLPUB/
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    Long titles should be typed on two lines without a blank line intervening. Put the title 2.5 cm from the top of the page. Write the title in [title case](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case); do not w
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    Write the title in [title case](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case); do not write the title in all capital letters, except for acronyms (e.g., "BLEU") or proper nouns ("English") that are normally up
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    Provide as complete a citation as possible, using a consistent format, such as the [one for Computational Linguistics](http://cljournal.org/style_guide_refs.html) or the one in the [Publication Manual of the American Psychological Associati
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    All references are required to contain DOIs of all cited works when possible, or, as a second resort, links to ACL Anthology pages. Appropriate records should be found for most materials in the current [ACL Anthology](https://aclweb.org/ant
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    Appropriate records should be found for most materials in the current [ACL Anthology](https://aclweb.org/anthology/).
  • Network accessline 263
    > Rie Kubota Ando and Tong Zhang. 2005. [A framework for learning predictive structures from multiple tasks and unlabeled data](https://www.jmlr.org/papers/v6/ando05a.html). *Journal of Machine Learning Research*, 6:1817–1853.
  • Network accessline 267
    > Galen Andrew and Jianfeng Gao. 2007. [Scalable training of L1-regularized log-linear models](https://doi.org/10.1145/1273496.1273501). In *Proceedings of the 24th International Conference on Machine Learning*, pages 33–40.
  • Network accessline 271
    > James Goodman, Andreas Vlachos, and Jason Naradowsky. 2016. [Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing](http://dx.doi.org/10.18653/v1/P16-1001). In *Proceedings of the 54th
  • Network accessline 275
    > Benjamin Börschinger and Mark Johnson. 2011. [A particle filter algorithm for Bayesian word segmentation](https://www.aclweb.org/anthology/U11-1004/). In *Proceedings of the Australasian Language Technology Association Workshop 2011*, pag
  • Network accessline 279
    > Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015. [Yara parser: A fast and accurate dependency parser](http://arxiv.org/abs/1503.06733). *Computing Research Repository*, arXiv:1503.06733. Version 2.

skills/research/ml-paper-writing/templates/colm2025/colm2025_conference.bib

  • Network accessline 5
    publisher = {Curran Associates, Inc.},  title = {Attention is All you Need},  url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},
  • Network accessline 6
    title = {Attention is All you Need},  url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},  volume = {30},
  • Network accessline 7
    url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},

skills/research/ml-paper-writing/templates/colm2025/colm2025_conference.tex

  • Network accessline 71
    COLM requires electronic submissions, processed by \url{https://openreview.net/}. See COLM's website for more instructions.
  • Network accessline 72
    \url{https://openreview.net/}. See COLM's website for more instructions.
  • Network accessline 102
    The style files for COLM and other conference information are available online at: \begin{center}    \url{http://www.colmweb.org/}
  • Network accessline 103
    \begin{center}    \url{http://www.colmweb.org/} \end{center}
  • Network accessline 104
    \url{http://www.colmweb.org/}
  • Network accessline 143
    We strongly recommend following arXiv's guidelines for making your paper friendly for HTML conversion: \url{https://info.arxiv.org/help/submit_latex_best_practices.html}.
  • Network accessline 277
    \end{verbatim} for .pdf graphics. See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})
  • Network accessline 279
    See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})

skills/research/ml-paper-writing/templates/colm2025/math_commands.tex

  • Dangerous shell operationline 82
    % rm is already a command, just don't name any random variables m

skills/research/ml-paper-writing/templates/iclr2026/iclr2026_conference.tex

  • Network accessline 5
    % Optional math commands from https://github.com/goodfeli/dlbook_notation.
  • Network accessline 65
    ICLR requires electronic submissions, processed by \url{https://openreview.net/}. See ICLR's website for more instructions.
  • Network accessline 66
    \url{https://openreview.net/}. See ICLR's website for more instructions.
  • Network accessline 93
    The style files for ICLR and other conference information are available online at: \begin{center}    \url{http://www.iclr.cc/}
  • Network accessline 94
    \begin{center}    \url{http://www.iclr.cc/} \end{center}
  • Network accessline 95
    \url{http://www.iclr.cc/}
  • Network accessline 230
    notation file from the textbook, \textit{Deep Learning} \cite{goodfellow2016deep} available at \url{https://github.com/goodfeli/dlbook_notation/}.  Use of this style
  • Network accessline 231
    \cite{goodfellow2016deep} available at \url{https://github.com/goodfeli/dlbook_notation/}.  Use of this style is not required and can be disabled by commenting out
  • Network accessline 232
    \url{https://github.com/goodfeli/dlbook_notation/}.  Use of this style
  • Network accessline 390
    \end{verbatim} for .pdf graphics. See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})
  • Network accessline 392
    See section~4.4 in the graphics bundle documentation (\url{http://www.ctan.org/tex-archive/macros/latex/required/graphics/grfguide.ps})

skills/research/ml-paper-writing/templates/iclr2026/math_commands.tex

  • Dangerous shell operationline 82
    % rm is already a command, just don't name any random variables m

skills/research/ml-paper-writing/templates/icml2026/example_paper.tex

  • Network accessline 136
    are available on the conference web site at: \begin{center}   \texttt{http://icml.cc/}
  • Network accessline 137
    \begin{center}   \texttt{http://icml.cc/} \end{center}
  • Network accessline 138
    \texttt{http://icml.cc/}
  • Network accessline 572
    for everyone including people with disabilities and sensory or neurological differences. Tips of how to achieve this and what to pay attention to will be provided on the conference website \url{http://icml.cc/}.
  • Network accessline 574
    provided on the conference website \url{http://icml.cc/}.

skills/research/ml-paper-writing/templates/icml2026/fancyhdr.sty

  • Network accessline 14
    %% license is in: %%  %%    http://www.latex-project.org/lppl.txt
  • Network accessline 15
    %%  %%    http://www.latex-project.org/lppl.txt %%
  • Network accessline 16
    %%    http://www.latex-project.org/lppl.txt

skills/research/ml-paper-writing/templates/neurips2025/Makefile

  • Executable scriptline 1
    Executable file
  • Network accessline 32
    upgrade: 	curl -O https://media.neurips.cc/Conferences/NeurIPS$(YEAR)/Styles.zip
  • Network accessline 33
    curl -O https://media.neurips.cc/Conferences/NeurIPS$(YEAR)/Styles.zip

skills/research/parallel-cli/SKILL.md

  • Dangerous shell operationline 66
    curl -fsSL https://parallel.ai/install.sh | bash
  • Network accessline 66
    curl -fsSL https://parallel.ai/install.sh | bash
  • Access to secretsline 93
    [Credential-related source omitted]
  • Network accessline 183
    parallel-cli extract https://example.com --json
  • Network accessline 184
    parallel-cli extract https://company.com --objective "Find pricing info" --json parallel-cli extract https://example.com --full-content --json parallel-cli fetch https://example.com --json
  • Network accessline 185
    parallel-cli extract https://example.com --full-content --json
  • Network accessline 186
    parallel-cli fetch https://example.com --json
  • Access to secretsline 362
    [Credential-related source omitted]

skills/research/polymarket/SKILL.md

  • Network accessline 14
    See `references/api-endpoints.md` for the full endpoint reference with curl examples.

skills/research/polymarket/scripts/polymarket.py

  • Executable scriptline 1
    #!/usr/bin/env python3
  • Network accessline 21
    GAMMA = "https://gamma-api.polymarket.com"
  • Network accessline 22
    CLOB = "https://clob.polymarket.com"
  • Network accessline 23
    DATA = "https://data-api.polymarket.com"
  • Network accessline 28
    req = urllib.request.Request(url, headers={"User-Agent": "gauss-agent/1.0"})
  • Network accessline 29
    try:         with urllib.request.urlopen(req, timeout=15) as resp:             return json.loads(resp.read().decode())
  • Network accessline 30
    with urllib.request.urlopen(req, timeout=15) as resp:

skills/smart-home/openhue/SKILL.md

  • Network accessline 8
    gauss:     tags: [Smart-Home, Hue, Lights, IoT, Automation]     homepage: https://www.openhue.io/cli
  • Network accessline 9
    tags: [Smart-Home, Hue, Lights, IoT, Automation]     homepage: https://www.openhue.io/cli prerequisites:
  • Network accessline 10
    homepage: https://www.openhue.io/cli
  • Network accessline 22
    # Linux (pre-built binary) curl -sL https://github.com/openhue/openhue-cli/releases/latest/download/openhue-linux-amd64 -o ~/.local/bin/openhue && chmod +x ~/.local/bin/openhue
  • Network accessline 23
    curl -sL https://github.com/openhue/openhue-cli/releases/latest/download/openhue-linux-amd64 -o ~/.local/bin/openhue && chmod +x ~/.local/bin/openhue

skills/social-media/xitter/SKILL.md

  • Access to secretsline 10
    [Credential-related source omitted]
  • Network accessline 12
    gauss:     tags: [twitter, x, social-media, x-cli]     homepage: https://github.com/Infatoshi/x-cli
  • Network accessline 13
    tags: [twitter, x, social-media, x-cli]     homepage: https://github.com/Infatoshi/x-cli ---
  • Network accessline 14
    homepage: https://github.com/Infatoshi/x-cli
  • Network accessline 39
    uv tool install git+https://github.com/Infatoshi/x-cli.git
  • Access to secretsline 57
    [Credential-related source omitted]
  • Access to secretsline 60
    [Credential-related source omitted]
  • Network accessline 63
    Get them from: - https://developer.x.com/en/portal/dashboard
  • Network accessline 64
    - https://developer.x.com/en/portal/dashboard
  • Access to secretsline 70
    [Credential-related source omitted]
  • Access to secretsline 72
    [Credential-related source omitted]
  • Access to secretsline 86
    [Credential-related source omitted]
  • Access to secretsline 99
    [Credential-related source omitted]
  • Access to secretsline 106
    [Credential-related source omitted]
  • Access to secretsline 107
    [Credential-related source omitted]
  • Access to secretsline 110
    [Credential-related source omitted]
  • Access to secretsline 113
    [Credential-related source omitted]
  • Network accessline 131
    x-cli tweet post "hello world" x-cli tweet get https://x.com/user/status/1234567890
  • Network accessline 132
    x-cli tweet get https://x.com/user/status/1234567890
  • Network accessline 200
    - Use tweet URLs or IDs interchangeably — `x-cli` accepts both. - If bookmark behavior changes upstream, check the upstream README first:   https://github.com/Infatoshi/x-cli
  • Network accessline 202
    https://github.com/Infatoshi/x-cli

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Some files could not be read; the facts below still hold.

Combined signals: 8 facts
Declared and executed: 4 facts
Signal confidence: 488 facts

and 438 more

Only the first 500 facts are listed.

Works with

Agent compatibility, from the files in this repository
AgentStatusEvidenceInstall to
Claude CodeCompatibleStandard SKILL.md optional-skills/autonomous-ai-agents/blackbox/SKILL.md.claude/skills/<skill-name>/
CodexNativeManifest AGENTS.md.agents/skills/<skill-name>/
CursorCompatibleStandard SKILL.md optional-skills/autonomous-ai-agents/blackbox/SKILL.md.cursor/skills/<skill-name>/
Gemini CLICompatibleStandard SKILL.md optional-skills/autonomous-ai-agents/blackbox/SKILL.md.gemini/skills/<skill-name>/
GitHub CopilotCompatibleStandard SKILL.md optional-skills/autonomous-ai-agents/blackbox/SKILL.md.github/skills/<skill-name>/
OpenCodeCompatibleStandard SKILL.md optional-skills/autonomous-ai-agents/blackbox/SKILL.md.opencode/skills/<skill-name>/
  • Native The repository ships a manifest, folder or frontmatter field for this agent.
  • Compatible A standard SKILL.md that this agent can read.
  • Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.

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