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graniet/kheish
Orchestration engine for long-running, tool-using AI agents
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
skills/apple/apple-notes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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skills/apple/apple-reminders/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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skills/apple/findmy/SKILL.md
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skills/apple/imessage/SKILL.md
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skills/autonomous-ai-agents/claude-code/SKILL.md
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skills/autonomous-ai-agents/codex/SKILL.md
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skills/autonomous-ai-agents/opencode/SKILL.md
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skills/creative/ascii-art/SKILL.md
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skills/creative/ascii-video/SKILL.md
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skills/creative/creative-ideation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/creative/excalidraw/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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skills/creative/manim-video/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
df50433f8d57 → df50433f8d57
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skills/creative/p5js/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/creative/popular-web-designs/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/creative/songwriting-and-ai-music/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/data-science/jupyter-live-kernel/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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skills/devops/webhook-subscriptions/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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skills/dogfood/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
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skills/email/himalaya/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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skills/gaming/minecraft-modpack-server/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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skills/gaming/pokemon-player/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
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skills/github/codebase-inspection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
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skills/github/github-auth/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/github/github-code-review/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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skills/github/github-issues/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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No earlier version was observed for comparison.
skills/github/github-pr-workflow/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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skills/github/github-repo-management/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/kheish-daemon-live-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/leisure/find-nearby/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mcp/mcporter/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mcp/native-mcp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/media/gif-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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No earlier version was observed for comparison.
skills/media/heartmula/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
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skills/media/songsee/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/media/youtube-content/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/cloud/modal/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/evaluation/lm-evaluation-harness/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/evaluation/weights-and-biases/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/huggingface-hub/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/gguf/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/guidance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/llama-cpp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/obliteratus/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/outlines/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/inference/vllm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/models/audiocraft/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/models/clip/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/models/segment-anything/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/models/stable-diffusion/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/models/whisper/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/research/dspy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/axolotl/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/grpo-rl-training/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/peft/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/pytorch-fsdp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/trl-fine-tuning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/mlops/training/unsloth/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/note-taking/obsidian/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/google-workspace/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/linear/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/nano-pdf/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Instructions only → After Instructions only
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/notion/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/ocr-and-documents/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/productivity/powerpoint/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Local scripts → After Local scripts
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Local scripts
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/red-teaming/godmode/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/research/arxiv/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/research/blogwatcher/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/research/llm-wiki/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/research/polymarket/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/research/research-paper-writing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/smart-home/openhue/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/social-media/xitter/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
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First observation → df50433f8d57
No earlier version was observed for comparison.
skills/software-development/plan/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → df50433f8d57
No earlier version was observed for comparison.
skills/software-development/requesting-code-review/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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df50433f8d57 → df50433f8d57
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → df50433f8d57
No earlier version was observed for comparison.
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 df50433f8d577f832c6812b9e67be8ddb766059d
git clone --filter=blob:none --no-checkout https://github.com/graniet/kheish.git .hotskills/graniet-kheish && git -C .hotskills/graniet-kheish checkout --detach df50433f8d577f832c6812b9e67be8ddb766059d
# Copy each selected skill directory from .hotskills/graniet-kheish into .agents/skills/<skill-name>/213GitHub stars
-4stars in 7 days
78skills in this repository
0commits in 30 days
Heat
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Individual skills in this repository
- apple-notes
skills/apple/apple-notes - apple-reminders
skills/apple/apple-reminders - findmy
skills/apple/findmy - imessage
skills/apple/imessage - claude-code
skills/autonomous-ai-agents/claude-code - codex
skills/autonomous-ai-agents/codex - opencode
skills/autonomous-ai-agents/opencode - ascii-art
skills/creative/ascii-art - ascii-video
skills/creative/ascii-video - creative-ideation
skills/creative/creative-ideation - excalidraw
skills/creative/excalidraw - manim-video
skills/creative/manim-video - p5js
skills/creative/p5js - popular-web-designs
skills/creative/popular-web-designs - songwriting-and-ai-music
skills/creative/songwriting-and-ai-music - jupyter-live-kernel
skills/data-science/jupyter-live-kernel - webhook-subscriptions
skills/devops/webhook-subscriptions - dogfood
skills/dogfood - himalaya
skills/email/himalaya - minecraft-modpack-server
skills/gaming/minecraft-modpack-server - pokemon-player
skills/gaming/pokemon-player - codebase-inspection
skills/github/codebase-inspection - github-auth
skills/github/github-auth - github-code-review
skills/github/github-code-review - github-issues
skills/github/github-issues - github-pr-workflow
skills/github/github-pr-workflow - github-repo-management
skills/github/github-repo-management - kheish-daemon-live-validation
skills/kheish-daemon-live-validation - find-nearby
skills/leisure/find-nearby - mcporter
skills/mcp/mcporter - native-mcp
skills/mcp/native-mcp - gif-search
skills/media/gif-search - heartmula
skills/media/heartmula - songsee
skills/media/songsee - youtube-content
skills/media/youtube-content - modal
skills/mlops/cloud/modal - lm-evaluation-harness
skills/mlops/evaluation/lm-evaluation-harness - weights-and-biases
skills/mlops/evaluation/weights-and-biases - huggingface-hub
skills/mlops/huggingface-hub - gguf
skills/mlops/inference/gguf - guidance
skills/mlops/inference/guidance - llama-cpp
skills/mlops/inference/llama-cpp - obliteratus
skills/mlops/inference/obliteratus - outlines
skills/mlops/inference/outlines - vllm
skills/mlops/inference/vllm - audiocraft
skills/mlops/models/audiocraft - clip
skills/mlops/models/clip - segment-anything
skills/mlops/models/segment-anything - stable-diffusion
skills/mlops/models/stable-diffusion - whisper
skills/mlops/models/whisper - dspy
skills/mlops/research/dspy - axolotl
skills/mlops/training/axolotl - grpo-rl-training
skills/mlops/training/grpo-rl-training - peft
skills/mlops/training/peft - pytorch-fsdp
skills/mlops/training/pytorch-fsdp - trl-fine-tuning
skills/mlops/training/trl-fine-tuning - unsloth
skills/mlops/training/unsloth - obsidian
skills/note-taking/obsidian - google-workspace
skills/productivity/google-workspace - linear
skills/productivity/linear - nano-pdf
skills/productivity/nano-pdf - notion
skills/productivity/notion - ocr-and-documents
skills/productivity/ocr-and-documents - powerpoint
skills/productivity/powerpoint - godmode
skills/red-teaming/godmode - arxiv
skills/research/arxiv - blogwatcher
skills/research/blogwatcher - llm-wiki
skills/research/llm-wiki - polymarket
skills/research/polymarket - research-paper-writing
skills/research/research-paper-writing - openhue
skills/smart-home/openhue - xitter
skills/social-media/xitter - plan
skills/software-development/plan - requesting-code-review
skills/software-development/requesting-code-review - subagent-driven-development
skills/software-development/subagent-driven-development - systematic-debugging
skills/software-development/systematic-debugging - test-driven-development
skills/software-development/test-driven-development - writing-plans
skills/software-development/writing-plans
How it works
When your agent uses it
Manage Apple Notes via the memo CLI on macOS (create, view, search, edit).
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
651 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Limitations
- Rules
Files
SKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytesSKILL.mdcore2,748 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Apple NotesWhen your agent uses it
Manage Apple Reminders via remindctl CLI (list, add, complete, delete).
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
806 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Date Formats
- Rules
Files
SKILL.mdcore3,106 bytesSKILL.mdcore3,106 bytesSKILL.mdcore3,106 bytesSKILL.mdcore3,106 bytesSKILL.mdcore3,106 bytesSKILL.mdcore3,106 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Apple RemindersWhen your agent uses it
Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
1,087 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- When to Use
- Method 1: AppleScript + Screenshot (Basic)
- Method 2: Peekaboo UI Automation (Recommended)
- Workflow: Track AirTag Location Over Time
- Limitations
- Rules
Files
SKILL.mdcore4,320 bytesSKILL.mdcore4,320 bytesSKILL.mdcore4,320 bytesSKILL.mdcore4,320 bytesSKILL.mdcore4,320 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Find My (Apple)When your agent uses it
Send and receive iMessages/SMS via the imsg CLI on macOS.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
790 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- When to Use
- When NOT to Use
- Quick Reference
- Service Options
- Rules
- Example Workflow
Files
SKILL.mdcore3,007 bytesSKILL.mdcore3,007 bytesSKILL.mdcore3,007 bytesSKILL.mdcore3,007 bytesSKILL.mdcore3,007 bytesSKILL.mdcore3,007 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# iMessageWhen your agent uses it
Delegate coding tasks to Claude Code (Anthropic's CLI agent). Use for building features, refactoring, PR reviews, and iterative coding. Requires the claude CLI installed.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
9,142 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- Two Orchestration Modes
- PTY Dialog Handling (CRITICAL for Interactive Mode)
- CLI Subcommands
- Print Mode Deep Dive
- Complete CLI Flags Reference
- Settings & Configuration
- Interactive Session: Slash Commands
- Interactive Session: Keyboard Shortcuts
- PR Review Pattern
- Parallel Claude Instances
- CLAUDE.md — Project Context File
- Custom Subagents
- Hooks — Automation on Events
- MCP Integration
- Monitoring Interactive Sessions
- Environment Variables
- Cost & Performance Tips
- Pitfalls & Gotchas
- Rules for Kheishs
Files
SKILL.mdcore34,927 bytesSKILL.mdcore34,927 bytesSKILL.mdcore34,927 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Claude Code — Kheish Orchestration GuideWhen your agent uses it
What it loads into context
Frontmatter
1,188 tokensAlways available to the agent
SKILL.md
1,190 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- One-Shot Tasks
- Background Mode (Long Tasks)
- Key Flags
- PR Reviews
- Parallel Issue Fixing with Worktrees
- Batch PR Reviews
- Rules
Files
SKILL.mdcore4,494 bytes
Read SKILL.md
---
description: Delegate coding tasks to OpenAI Codex CLI agent. Use for building features,
refactoring, PR reviews, and batch issue fixing. Requires the codex CLI and a git
repository.
version: 1.0.0
license: MIT
metadata:
catalog:
tags:
- Coding-Agent
- Codex
- OpenAI
- Code-Review
- Refactoring
related_skills:When your agent uses it
What it loads into context
Frontmatter
1,982 tokensAlways available to the agent
SKILL.md
1,983 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Prerequisites
- Binary Resolution (Important)
- One-Shot Tasks
- Interactive Sessions (Background)
- Common Flags
- Procedure
- PR Review Workflow
- Parallel Work Pattern
- Session & Cost Management
- Pitfalls
- Verification
- Rules
Files
SKILL.mdcore7,922 bytesSKILL.mdcore7,922 bytes
Read SKILL.md
---
description: Delegate coding tasks to OpenCode CLI agent for feature implementation,
refactoring, PR review, and long-running autonomous sessions. Requires the opencode
CLI installed and authenticated.
version: 1.2.0
license: MIT
metadata:
catalog:
tags:
- Coding-Agent
- OpenCode
- Autonomous
- Refactoring
- Code-Review
related_skills:When your agent uses it
Generate ASCII art using pyfiglet (571 fonts), cowsay, boxes, toilet, image-to-ascii, remote APIs (asciified, ascii.co.uk), and LLM fallback. No API keys required.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
3,187 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Tool 1: Text Banners (pyfiglet — local)
- Tool 2: Text Banners (asciified API — remote, no install)
- Tool 3: Cowsay (Message Art)
- Tool 4: Boxes (Decorative Borders)
- Tool 5: TOIlet (Colored Text Art)
- Tool 6: Image to ASCII Art
- Tool 7: Search Pre-Made ASCII Art
- Tool 8: Fun ASCII Utilities (via curl)
- Tool 9: LLM-Generated Custom Art (Fallback)
- Decision Flow
Files
SKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytesSKILL.mdcore11,217 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# ASCII Art SkillWhen your agent uses it
Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
3,562 tokensWhen the skill is activated
Reference documents
93,366 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Creative Standard
- Modes
- Stack
- Pipeline Architecture
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Files
README.mddoc16,259 bytesSKILL.mdcore15,302 bytesSKILL.mdcore15,302 bytesSKILL.mdcore15,302 bytesSKILL.mdcore15,302 bytesreferences/architecture.mddoc33,004 bytesreferences/composition.mddoc34,942 bytesreferences/effects.mddoc72,928 bytesreferences/inputs.mddoc24,575 bytesreferences/optimization.mddoc24,265 bytesreferences/scenes.mddoc37,487 bytesreferences/shaders.mddoc50,435 bytesreferences/troubleshooting.mddoc13,246 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# ASCII Video Production PipelineWhen your agent uses it
Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
1,615 tokensWhen the skill is activated
Reference documents
959 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- How It Works
- The Rule
- Constraint Library
- Matching Constraints to Users
- Output Format
- Example
- Attribution
Files
SKILL.mdcore6,858 bytesreferences/full-prompt-library.mddoc4,215 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Creative IdeationWhen your agent uses it
Create hand-drawn style diagrams using Excalidraw JSON format. Generate .excalidraw files for architecture diagrams, flowcharts, sequence diagrams, concept maps, and more. Files can be opened at excalidraw.com or uploaded for shareable links.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
2,390 tokensWhen the skill is activated
Reference documents
6,068 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Workflow
- Element Format Reference
Scripts executed separately
scripts/upload.py
Files
SKILL.mdcore7,820 bytesSKILL.mdcore7,820 bytesreferences/colors.mddoc1,777 bytesreferences/dark-mode.mddoc2,282 bytesreferences/examples.mddoc14,220 bytesscripts/upload.pyruns3,817 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Excalidraw Diagram SkillWhen your agent uses it
Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
3,185 tokensWhen the skill is activated
Reference documents
23,760 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Creative Standard
- Prerequisites
- Modes
- Stack
- Pipeline
- Project Structure
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Scripts executed separately
scripts/setup.sh
Files
README.mddoc886 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesSKILL.mdcore12,549 bytesreferences/animation-design-thinking.mddoc7,205 bytesreferences/animations.mddoc8,836 bytesreferences/camera-and-3d.mddoc4,132 bytesreferences/decorations.mddoc5,006 bytesreferences/equations.mddoc6,086 bytesreferences/graphs-and-data.mddoc4,628 bytesreferences/mobjects.mddoc9,739 bytesreferences/paper-explainer.mddoc9,333 bytesreferences/production-quality.mddoc5,904 bytesreferences/rendering.mddoc5,375 bytesreferences/scene-planning.mddoc2,751 bytesreferences/troubleshooting.mddoc4,319 bytesreferences/updaters-and-trackers.mddoc8,500 bytesreferences/visual-design.mddoc4,762 bytesscripts/setup.shruns921 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Manim Video Production PipelineWhen your agent uses it
Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems, flow fields, shaders (GLSL), pixel manipulation, kinetic typography, WebGL scenes, audio analysis, mouse/keyboard interaction, and headless high-res export. Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
6,993 tokensWhen the skill is activated
Reference documents
34,392 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Creative Standard
- Modes
- Stack
- Pipeline
- Creative Direction
- Workflow
- Critical Implementation Notes
- Performance Targets
- References
- Creative Divergence (use only when user requests experimental/creative/unique output)
Scripts executed separately
scripts/export-frames.jsscripts/render.shscripts/serve.shscripts/setup.sh
Files
README.mddoc3,316 bytesSKILL.mdcore28,013 bytesreferences/animation.mddoc10,456 bytesreferences/color-systems.mddoc8,647 bytesreferences/core-api.mddoc9,841 bytesreferences/export-pipeline.mddoc15,611 bytesreferences/interaction.mddoc8,345 bytesreferences/shapes-and-geometry.mddoc7,213 bytesreferences/troubleshooting.mddoc13,041 bytesreferences/typography.mddoc6,779 bytesreferences/visual-effects.mddoc23,456 bytesreferences/webgl-and-3d.mddoc8,893 bytesscripts/export-frames.jsruns5,398 bytesscripts/render.shruns2,911 bytesscripts/serve.shruns833 bytesscripts/setup.shruns2,475 bytestemplates/viewer.htmlasset10,858 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# p5.js Production PipelineWhen your agent uses it
54 production-quality design systems extracted from real websites. Load a template to generate HTML/CSS that matches the visual identity of sites like Stripe, Linear, Vercel, Notion, Airbnb, and more. Each template includes colors, typography, components, layout rules, and ready-to-use CSS values.
What it loads into context
Frontmatter
158 tokensAlways available to the agent
SKILL.md
2,476 tokensWhen the skill is activated
Reference documents
239,288 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- How to Use
- HTML Generation Pattern
- Font Substitution Reference
- Design Catalog
- Choosing a Design
Files
SKILL.mdcore9,890 bytestemplates/airbnb.mddoc14,818 bytestemplates/airtable.mddoc4,294 bytestemplates/apple.mddoc21,116 bytestemplates/bmw.mddoc10,579 bytestemplates/cal.mddoc18,576 bytestemplates/claude.mddoc21,072 bytestemplates/clay.mddoc18,099 bytestemplates/clickhouse.mddoc16,174 bytestemplates/cohere.mddoc15,537 bytestemplates/coinbase.mddoc5,775 bytestemplates/composio.mddoc21,726 bytestemplates/cursor.mddoc19,861 bytestemplates/elevenlabs.mddoc15,972 bytestemplates/expo.mddoc19,460 bytestemplates/figma.mddoc12,397 bytestemplates/framer.mddoc18,341 bytestemplates/hashicorp.mddoc16,419 bytestemplates/ibm.mddoc20,360 bytestemplates/intercom.mddoc7,156 bytestemplates/kraken.mddoc5,088 bytestemplates/linear.app.mddoc23,225 bytestemplates/lovable.mddoc18,154 bytestemplates/minimax.mddoc15,065 bytestemplates/mintlify.mddoc18,197 bytestemplates/miro.mddoc5,532 bytestemplates/mistral.ai.mddoc16,415 bytestemplates/mongodb.mddoc15,802 bytestemplates/notion.mddoc19,084 bytestemplates/nvidia.mddoc17,828 bytestemplates/ollama.mddoc16,301 bytestemplates/opencode.ai.mddoc16,301 bytestemplates/pinterest.mddoc12,688 bytestemplates/posthog.mddoc19,341 bytestemplates/raycast.mddoc19,255 bytestemplates/replicate.mddoc14,941 bytestemplates/resend.mddoc19,195 bytestemplates/revolut.mddoc9,357 bytestemplates/runwayml.mddoc15,091 bytestemplates/sanity.mddoc22,035 bytestemplates/sentry.mddoc15,533 bytestemplates/spacex.mddoc12,058 bytestemplates/spotify.mddoc13,725 bytestemplates/stripe.mddoc21,257 bytestemplates/supabase.mddoc15,586 bytestemplates/superhuman.mddoc19,708 bytestemplates/together.ai.mddoc15,067 bytestemplates/uber.mddoc19,424 bytestemplates/vercel.mddoc20,221 bytestemplates/voltagent.mddoc27,085 bytestemplates/warp.mddoc17,435 bytestemplates/webflow.mddoc4,857 bytestemplates/wise.mddoc9,202 bytestemplates/x.ai.mddoc17,037 bytestemplates/zapier.mddoc19,478 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Popular Web DesignsWhen your agent uses it
Songwriting craft, AI music generation prompts (Suno focus), parody/adaptation techniques, phonetic tricks, and lessons learned. These are tools and ideas, not rules. Break any of them when the art calls for it.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
2,792 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- 1. Song Structure (Pick One or Invent Your Own)
- 2. Rhyme, Meter, and Sound
- 3. Emotional Arc and Dynamics
- 4. Writing Lyrics That Work
- 5. Parody and Adaptation
- 6. Suno AI Prompt Engineering
- 7. Phonetic Tricks for AI Singers
- 8. Workflow
- 9. Lessons Learned
Files
SKILL.mdcore10,880 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Songwriting & AI Music GenerationWhen your agent uses it
Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.
What it loads into context
Frontmatter
137 tokensAlways available to the agent
SKILL.md
1,589 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This vs Other Tools
- Prerequisites
- Setup
- Core Workflow
- Practical Tips from Experience
- Timeout Defaults
Files
SKILL.mdcore6,268 bytesSKILL.mdcore6,268 bytesSKILL.mdcore6,268 bytesSKILL.mdcore6,268 bytesSKILL.mdcore6,268 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Jupyter Live Kernel (hamelnb)When your agent uses it
Create and manage webhook subscriptions for event-driven agent activation. Use when the user wants external services to trigger agent runs automatically.
What it loads into context
Frontmatter
59 tokensAlways available to the agent
SKILL.md
1,603 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Setup (Required First)
- Commands
- Prompt Templates
- Common Patterns
- Security
- How It Works
- Troubleshooting
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
Kheish does not currently ship a dedicated webhook subscription CLI. In the examples below,When your agent uses it
Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,574 tokensWhen the skill is activated
Reference documents
1,143 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- Prerequisites
- Inputs
- Workflow
- Tools Reference
- Tips
Files
SKILL.mdcore6,881 bytesreferences/issue-taxonomy.mddoc3,682 bytestemplates/dogfood-report-template.mddoc1,522 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Dogfood: Systematic Web Application QA TestingWhen your agent uses it
CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
1,647 tokensWhen the skill is activated
Reference documents
1,969 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- References
- Prerequisites
- Configuration Setup
- Kheish Integration Notes
- Common Operations
- Multiple Accounts
- Attachments
- Output Formats
- Debugging
- Tips
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Himalaya Email CLIWhen your agent uses it
Set up a modded Minecraft server from a CurseForge/Modrinth server pack zip. Covers NeoForge/Forge install, Java version, JVM tuning, firewall, LAN config, backups, and launch scripts.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
1,999 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use
- Gather User Preferences First
- Steps
- Pitfalls
- Verification
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Minecraft Modpack Server SetupWhen your agent uses it
Play Pokemon games autonomously via headless emulation. Starts a game server, reads structured game state from RAM, makes strategic decisions, and sends button inputs — all from the terminal.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,298 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Startup Procedure
- Save and Load
- The Gameplay Loop
- Action Reference
- Critical Tips from Experience
- Battle Strategy
- Memory Conventions
- Progression Milestones
- Stopping Play
- Pitfalls
Files
SKILL.mdcore9,381 bytesSKILL.mdcore9,381 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Pokemon PlayerWhen your agent uses it
Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
1,112 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Prerequisites
- 1. Basic Summary (Most Common)
- 2. Common Folder Exclusions
- 3. Filter by Specific Language
- 4. Detailed File-by-File Output
- 5. Output Formats
- 6. Interpreting Results
- Pitfalls
Files
SKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytesSKILL.mdcore4,305 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Codebase Inspection with pygountWhen your agent uses it
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
1,962 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Detection Flow
- Method 1: Git-Only Authentication (No gh, No sudo)
- Method 2: gh CLI Authentication
- Using the GitHub API Without gh
- Troubleshooting
Scripts executed separately
scripts/gh-env.sh
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GitHub Authentication SetupWhen your agent uses it
Review code changes by analyzing git diffs, leaving inline comments on PRs, and performing thorough pre-push review. Works with gh CLI or falls back to git + GitHub REST API via curl.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
3,725 tokensWhen the skill is activated
Reference documents
564 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- 1. Reviewing Local Changes (Pre-Push)
- 2. Reviewing a Pull Request on GitHub
- 3. Review Checklist
- 4. Pre-Push Review Workflow
- 5. PR Review Workflow (End-to-End)
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GitHub Code ReviewWhen your agent uses it
Create, manage, triage, and close GitHub issues. Search existing issues, add labels, assign people, and link to PRs. Works with gh CLI or falls back to git + GitHub REST API via curl.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
2,938 tokensWhen the skill is activated
Reference documents
229 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- 1. Viewing Issues
- 2. Creating Issues
- 3. Managing Issues
- 4. Issue Triage Workflow
- 5. Bulk Operations
- Quick Reference Table
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GitHub Issues ManagementWhen your agent uses it
Full pull request lifecycle — create branches, commit changes, open PRs, monitor CI status, auto-fix failures, and merge. Works with gh CLI or falls back to git + GitHub REST API via curl.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
2,939 tokensWhen the skill is activated
Reference documents
2,058 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- 1. Branch Creation
- 2. Making Commits
- 3. Pushing and Creating a PR
- 4. Monitoring CI Status
- 5. Auto-Fixing CI Failures
- 6. Merging
- 7. Complete Workflow Example
- Useful PR Commands Reference
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GitHub Pull Request WorkflowWhen your agent uses it
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
4,206 tokensWhen the skill is activated
Reference documents
1,729 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- 1. Cloning Repositories
- 2. Creating Repositories
- 3. Forking Repositories
- 4. Repository Information
- 5. Repository Settings
- 6. Branch Protection
- 7. Secrets Management (GitHub Actions)
- 8. Releases
- 9. GitHub Actions Workflows
- 10. Gists
- Quick Reference Table
Files
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Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GitHub Repository ManagementWhen your agent uses it
Return the deterministic readiness marker used by live daemon validation scenarios.
What it loads into context
Frontmatter
39 tokensAlways available to the agent
SKILL.md
76 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
Files
SKILL.mdcore341 bytes
Read SKILL.md
You are using the `kheish-daemon-live-validation` skill.
Reply with exactly `KHEISH_DAEMON_VALIDATION_SKILL_OK` and nothing else.When your agent uses it
Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
935 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick Reference
- Workflow
- Tips
Scripts executed separately
scripts/find_nearby.py
Files
SKILL.mdcore3,678 bytesSKILL.mdcore3,678 bytesSKILL.mdcore3,678 bytesSKILL.mdcore3,678 bytesSKILL.mdcore3,678 bytesSKILL.mdcore3,678 bytesscripts/find_nearby.pyruns6,803 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Find Nearby — Local Place DiscoveryWhen your agent uses it
Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, and CLI/type generation.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
941 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- Quick Start
- Discovering MCP Servers
- Calling Tools
- Auth and Config
- Daemon
- Code Generation
- Notes
Files
SKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytesSKILL.mdcore3,740 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# mcporterWhen your agent uses it
Built-in MCP (Model Context Protocol) client that connects to external MCP servers, discovers their tools, and registers them as native Kheish tools. Supports stdio and HTTP transports with automatic reconnection, security filtering, and zero-config tool injection.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,137 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Prerequisites
- Quick Start
- Configuration Reference
- How It Works
- Transport Types
- Security
- Troubleshooting
- Examples
- Sampling (Server-Initiated LLM Requests)
- Notes
Files
SKILL.mdcore13,106 bytesSKILL.mdcore13,106 bytesSKILL.mdcore13,106 bytesSKILL.mdcore13,106 bytesSKILL.mdcore13,106 bytesSKILL.mdcore13,106 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Native MCP ClientWhen your agent uses it
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.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
924 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Setup
- Prerequisites
- Search for GIFs
- Download a GIF
- Get Full Metadata
- API Parameters
- Available Media Formats
- Notes
Files
SKILL.mdcore3,259 bytesSKILL.mdcore3,259 bytesSKILL.mdcore3,259 bytesSKILL.mdcore3,259 bytesSKILL.mdcore3,259 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GIF Search (Tenor API)When your agent uses it
Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,885 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- When to Use
- Hardware Requirements
- Installation Steps
- GPU / CUDA
- Usage
- Pitfalls
- Links
Files
SKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytesSKILL.mdcore7,006 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# HeartMuLa - Open-Source Music GenerationWhen your agent uses it
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
797 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- Quick Start
- Visualization Types
- Common Flags
- Notes
Files
SKILL.mdcore3,032 bytesSKILL.mdcore3,032 bytesSKILL.mdcore3,032 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# songseeWhen your agent uses it
Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
830 tokensWhen the skill is activated
Reference documents
315 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Setup
- Helper Script
- Output Formats
- Workflow
- Error Handling
Scripts executed separately
scripts/fetch_transcript.py
Files
SKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesSKILL.mdcore3,700 bytesreferences/output-formats.mddoc1,259 bytesscripts/fetch_transcript.pyruns4,041 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# YouTube Content ToolWhen your agent uses it
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,291 tokensWhen the skill is activated
Reference documents
5,169 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use Modal
- Quick start
- Core concepts
- GPU configuration
- Container images
- Persistent storage
- Web endpoints
- Dynamic batching
- Secrets management
- Scheduling
- Performance optimization
- Parallel processing
- Common configuration
- Debugging
- Common issues
- References
- Resources
Files
SKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesSKILL.mdcore9,160 bytesreferences/advanced-usage.mddoc10,903 bytesreferences/troubleshooting.mddoc10,516 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Modal Serverless GPUWhen your agent uses it
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
3,625 tokensWhen the skill is activated
Reference documents
13,475 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
SKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesSKILL.mdcore12,518 bytesreferences/api-evaluation.mddoc11,114 bytesreferences/benchmark-guide.mddoc10,769 bytesreferences/custom-tasks.mddoc13,125 bytesreferences/distributed-eval.mddoc11,426 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# lm-evaluation-harness - LLM BenchmarkingWhen your agent uses it
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
3,423 tokensWhen the skill is activated
Reference documents
12,074 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Installation
- Quick Start
- Core Concepts
- Hyperparameter Sweeps
- Artifacts
- Integration Examples
- Visualization & Analysis
- Best Practices
- Team Collaboration
- Pricing
- Resources
- See Also
Files
SKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesSKILL.mdcore13,084 bytesreferences/artifacts.mddoc13,425 bytesreferences/integrations.mddoc16,219 bytesreferences/sweeps.mddoc17,675 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Weights & Biases: ML Experiment Tracking & MLOpsWhen your agent uses it
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
1,037 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick Start
- Core Commands
- Specialized Hub Interactions
- Advanced Usage & Tips
Files
SKILL.mdcore4,311 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Hugging Face CLI (`hf`) Reference GuideWhen your agent uses it
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
3,289 tokensWhen the skill is activated
Reference documents
5,602 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use GGUF
- Quick start
- Quantization types
- Conversion workflows
- Python usage
- Server mode
- Hardware optimization
- Integration with tools
- Best practices
- Common issues
- References
- Resources
Files
SKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesSKILL.mdcore10,933 bytesreferences/advanced-usage.mddoc10,887 bytesreferences/troubleshooting.mddoc8,904 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GGUF - Quantization Format for llama.cppWhen your agent uses it
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
What it loads into context
Frontmatter
121 tokensAlways available to the agent
SKILL.md
4,135 tokensWhen the skill is activated
Reference documents
14,778 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Installation
- Quick Start
- Core Concepts
- Backend Configuration
- Common Patterns
- Best Practices
- Comparison to Alternatives
- Performance Characteristics
- Resources
- See Also
Files
SKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesSKILL.mdcore15,146 bytesreferences/backends.mddoc12,010 bytesreferences/constraints.mddoc15,886 bytesreferences/examples.mddoc20,079 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Guidance: Constrained LLM GenerationWhen your agent uses it
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
What it loads into context
Frontmatter
170 tokensAlways available to the agent
SKILL.md
2,084 tokensWhen the skill is activated
Reference documents
2,929 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use llama.cpp
- Quick start
- Quantization formats
- Hardware acceleration
- Common patterns
- Performance benchmarks
- Supported models
- References
- Resources
Files
SKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesSKILL.mdcore6,553 bytesreferences/optimization.mddoc1,659 bytesreferences/quantization.mddoc4,956 bytesreferences/server.mddoc2,259 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# llama.cppWhen your agent uses it
Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
What it loads into context
Frontmatter
234 tokensAlways available to the agent
SKILL.md
4,033 tokensWhen the skill is activated
Reference documents
2,918 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Step 1: Installation
- Step 2: Check Hardware
- Step 3: Browse Available Models & Get Recommendations
- Step 4: Choose a Method
- Step 5: Run Abliteration
- Step 6: Verify Results
- Step 7: Use the Abliterated Model
- CLI Command Reference
- Analysis Modules
- Ablation Strategies
- Evaluation
- Platform Support
- YAML Config Templates
- Telemetry
- Common Pitfalls
- Complementary Skills
Files
SKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesSKILL.mdcore15,969 bytesreferences/analysis-modules.mddoc6,270 bytesreferences/methods-guide.mddoc6,719 bytestemplates/abliteration-config.yamlasset1,189 bytestemplates/analysis-study.yamlasset1,368 bytestemplates/batch-abliteration.yamlasset1,201 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# OBLITERATUS SkillWhen your agent uses it
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
What it loads into context
Frontmatter
148 tokensAlways available to the agent
SKILL.md
4,190 tokensWhen the skill is activated
Reference documents
13,253 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Installation
- Quick Start
- Core Concepts
- Common Patterns
- Backend Configuration
- Best Practices
- Comparison to Alternatives
- Performance Characteristics
- Resources
- See Also
Files
SKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesSKILL.mdcore16,589 bytesreferences/backends.mddoc14,407 bytesreferences/examples.mddoc21,231 bytesreferences/json_generation.mddoc14,927 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Outlines: Structured Text GenerationWhen your agent uses it
Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
What it loads into context
Frontmatter
161 tokensAlways available to the agent
SKILL.md
2,643 tokensWhen the skill is activated
Reference documents
7,720 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
SKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesSKILL.mdcore9,650 bytesreferences/optimization.mddoc5,769 bytesreferences/quantization.mddoc6,746 bytesreferences/server-deployment.mddoc5,252 bytesreferences/troubleshooting.mddoc9,037 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# vLLM - High-Performance LLM ServingWhen your agent uses it
PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
What it loads into context
Frontmatter
130 tokensAlways available to the agent
SKILL.md
3,896 tokensWhen the skill is activated
Reference documents
7,065 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use AudioCraft
- Quick start
- Core concepts
- MusicGen usage
- MusicGen-Style usage
- AudioGen usage
- EnCodec usage
- Common workflows
- Performance optimization
- Common issues
- References
- Resources
Files
SKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesSKILL.mdcore16,903 bytesreferences/advanced-usage.mddoc16,953 bytesreferences/troubleshooting.mddoc11,412 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# AudioCraft: Audio GenerationWhen your agent uses it
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
What it loads into context
Frontmatter
153 tokensAlways available to the agent
SKILL.md
1,907 tokensWhen the skill is activated
Reference documents
1,365 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use CLIP
- Quick start
- Available models
- Image-text similarity
- Semantic image search
- Content moderation
- Batch processing
- Integration with vector databases
- Best practices
- Performance
- Limitations
- Resources
Files
SKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesSKILL.mdcore7,522 bytesreferences/applications.mddoc5,360 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# CLIP - Contrastive Language-Image Pre-TrainingWhen your agent uses it
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
3,529 tokensWhen the skill is activated
Reference documents
7,051 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use SAM
- Quick start
- Core concepts
- Interactive segmentation
- Automatic mask generation
- Batched inference
- ONNX deployment
- Common workflows
- Output format
- Performance optimization
- Common issues
- References
- Resources
Files
SKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesSKILL.mdcore14,056 bytesreferences/advanced-usage.mddoc15,803 bytesreferences/troubleshooting.mddoc11,914 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Segment Anything Model (SAM)When your agent uses it
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
3,452 tokensWhen the skill is activated
Reference documents
7,636 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use Stable Diffusion
- Quick start
- Architecture overview
- Core concepts
- Generation parameters
- Image-to-image
- Inpainting
- ControlNet
- LoRA adapters
- Memory optimization
- Model variants
- Batch generation
- Common workflows
- Common issues
- References
- Resources
Files
SKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesSKILL.mdcore13,584 bytesreferences/advanced-usage.mddoc17,690 bytesreferences/troubleshooting.mddoc12,401 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Stable Diffusion Image GenerationWhen your agent uses it
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
What it loads into context
Frontmatter
161 tokensAlways available to the agent
SKILL.md
2,184 tokensWhen the skill is activated
Reference documents
1,329 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use Whisper
- Quick start
- Model sizes
- Transcription options
- Command line usage
- Batch processing
- Real-time transcription
- GPU acceleration
- Integration with other tools
- Best practices
- Performance
- Language support
- Limitations
- Resources
Files
SKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesSKILL.mdcore8,106 bytesreferences/languages.mddoc4,786 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Whisper - Robust Speech RecognitionWhen your agent uses it
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
3,890 tokensWhen the skill is activated
Reference documents
10,990 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Installation
- Quick Start
- Core Concepts
- LM Provider Configuration
- Common Patterns
- Evaluation and Metrics
- Best Practices
- Comparison to Other Approaches
- Resources
- See Also
Files
SKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesSKILL.mdcore15,932 bytesreferences/examples.mddoc18,983 bytesreferences/modules.mddoc12,363 bytesreferences/optimizers.mddoc14,933 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# DSPy: Declarative Language Model ProgrammingWhen your agent uses it
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
What it loads into context
Frontmatter
169 tokensAlways available to the agent
SKILL.md
1,305 tokensWhen the skill is activated
Reference documents
74,597 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
SKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesSKILL.mdcore5,452 bytesreferences/api.mddoc121,144 bytesreferences/dataset-formats.mddoc46,043 bytesreferences/index.mddoc199 bytesreferences/other.mddoc140,405 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Axolotl SkillWhen your agent uses it
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
4,435 tokensWhen the skill is activated
Reference documents
847 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Core Concepts
- Implementation Workflow
- Critical Training Insights
- Advanced Patterns
- Deployment and Inference
- Best Practices Checklist
- Troubleshooting Guide
- References and Resources
- Usage Instructions for Agents
Scripts executed separately
templates/basic_grpo_training.py
Files
README.mddoc3,514 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytesSKILL.mdcore17,828 bytestemplates/basic_grpo_training.pyruns6,113 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# GRPO/RL Training with TRLWhen your agent uses it
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
3,613 tokensWhen the skill is activated
Reference documents
5,929 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to use PEFT
- Quick start
- LoRA parameter selection
- Loading and merging adapters
- PEFT methods comparison
- Integration patterns
- Performance benchmarks
- Common issues
- Best practices
- References
- Resources
Files
SKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesSKILL.mdcore12,839 bytesreferences/advanced-usage.mddoc12,541 bytesreferences/troubleshooting.mddoc10,344 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# PEFT (Parameter-Efficient Fine-Tuning)When your agent uses it
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
36,474 tokensWhen the skill is activated
Reference documents
69,791 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
SKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesSKILL.mdcore160,770 bytesreferences/index.mddoc96 bytesreferences/other.mddoc337,500 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Pytorch-Fsdp SkillWhen your agent uses it
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
What it loads into context
Frontmatter
175 tokensAlways available to the agent
SKILL.md
3,238 tokensWhen the skill is activated
Reference documents
3,465 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick start
- Common workflows
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
Files
SKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesSKILL.mdcore12,085 bytesreferences/dpo-variants.mddoc4,297 bytesreferences/online-rl.mddoc1,971 bytesreferences/reward-modeling.mddoc2,597 bytesreferences/sft-training.mddoc3,237 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# TRL - Transformer Reinforcement LearningWhen your agent uses it
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
647 tokensWhen the skill is activated
Reference documents
556,209 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Quick Reference
- Reference Files
- Working with This Skill
- Resources
- Notes
- Updating
Files
SKILL.mdcore2,950 bytesSKILL.mdcore2,950 bytesreferences/index.mddoc98 bytesreferences/llms-full.mddoc1,077,225 bytesreferences/llms-txt.mddoc813,089 bytesreferences/llms.mddoc12,504 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Unsloth SkillWhen your agent uses it
Read, search, and create notes in the Obsidian vault.
What it loads into context
Frontmatter
16 tokensAlways available to the agent
SKILL.md
507 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Read a note
- List notes
- Search
- Create a note
- Append to a note
- Wikilinks
Files
SKILL.mdcore1,823 bytesSKILL.mdcore1,823 bytesSKILL.mdcore1,823 bytesSKILL.mdcore1,823 bytesSKILL.mdcore1,823 bytesSKILL.mdcore1,823 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Obsidian VaultWhen your agent uses it
Gmail, Calendar, Drive, Contacts, Sheets, and Docs integration via gws CLI (googleworkspace/cli). Uses OAuth2 with automatic token refresh via bridge script. Requires gws binary.
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
2,123 tokensWhen the skill is activated
Reference documents
651 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Architecture
- References
- Scripts
- Prerequisites
- First-Time Setup
- Usage
- Output Format
- Rules
- Troubleshooting
- Revoking Access
Scripts executed separately
scripts/google_api.pyscripts/gws_bridge.pyscripts/setup.py
Files
SKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesSKILL.mdcore7,869 bytesreferences/gmail-search-syntax.mddoc2,150 bytesscripts/google_api.pyruns10,484 bytesscripts/gws_bridge.pyruns2,703 bytesscripts/setup.pyruns14,753 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Google WorkspaceWhen your agent uses it
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.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,346 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Setup
- API Basics
- Workflow States
- Common Queries
- Common Mutations
- Pagination
- Filtering Reference
- Typical Workflow
- Rate Limits
- Important Notes
Files
SKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytesSKILL.mdcore11,910 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Linear — Issue & Project ManagementWhen your agent uses it
Edit PDFs with natural-language instructions using the nano-pdf CLI. Modify text, fix typos, update titles, and make content changes to specific pages without manual editing.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
517 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- Usage
- Examples
- Notes
Files
SKILL.mdcore2,082 bytesSKILL.mdcore2,082 bytesSKILL.mdcore2,082 bytesSKILL.mdcore2,082 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# nano-pdfWhen your agent uses it
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
1,747 tokensWhen the skill is activated
Reference documents
890 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- API Basics
- Common Operations
- Property Types
- Key Differences in API Version 2025-09-03
- Notes
Files
SKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesSKILL.mdcore5,890 bytesreferences/block-types.mddoc3,021 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Notion APIWhen your agent uses it
Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
1,588 tokensWhen the skill is activated
Reference documents
27 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Step 1: Remote URL Available?
- Step 2: Choose Local Extractor
- pymupdf (lightweight)
- marker-pdf (high-quality OCR)
- Arxiv Papers
- Split, Merge & Search
- Notes
Scripts executed separately
scripts/extract_marker.pyscripts/extract_pymupdf.py
Files
DESCRIPTION.mddoc147 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesSKILL.mdcore5,966 bytesscripts/extract_marker.pyruns3,034 bytesscripts/extract_pymupdf.pyruns3,132 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# PDF & Document ExtractionWhen your agent uses it
Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
2,524 tokensWhen the skill is activated
Reference documents
6,101 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick Reference
- Reading Content
- Editing Workflow
- Creating from Scratch
- Design Ideas
- QA (Required)
- Converting to Images
- Dependencies
Scripts executed separately
scripts/__init__.pyscripts/add_slide.pyscripts/clean.pyscripts/office/helpers/__init__.pyscripts/office/helpers/merge_runs.pyscripts/office/helpers/simplify_redlines.pyscripts/office/pack.py
Files
LICENSE.txtdoc1,467 bytesSKILL.mdcore9,775 bytesSKILL.mdcore9,775 bytesSKILL.mdcore9,775 bytesSKILL.mdcore9,775 bytesSKILL.mdcore9,775 bytesSKILL.mdcore9,775 bytesediting.mddoc6,885 bytespptxgenjs.mddoc12,819 bytesscripts/__init__.pyruns0 bytesscripts/add_slide.pyruns6,872 bytesscripts/clean.pyruns9,583 bytesscripts/office/helpers/__init__.pyruns0 bytesscripts/office/helpers/merge_runs.pyruns5,567 bytesscripts/office/helpers/simplify_redlines.pyruns5,754 bytesscripts/office/pack.pyruns4,991 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsdasset74,984 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsdasset6,956 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsdasset51,302 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-lockedCanvas.xsdasset624 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-main.xsdasset152,039 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-picture.xsdasset1,231 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-spreadsheetDrawing.xsdasset8,862 bytesscripts/office/schemas/ISO-IEC29500-4_2016/dml-wordprocessingDrawing.xsdasset14,795 bytesscripts/office/schemas/ISO-IEC29500-4_2016/pml.xsdasset83,612 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-additionalCharacteristics.xsdasset1,269 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-bibliography.xsdasset7,328 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-commonSimpleTypes.xsdasset6,382 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlDataProperties.xsdasset1,248 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-customXmlSchemaProperties.xsdasset880 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesCustom.xsdasset2,608 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesExtended.xsdasset3,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-documentPropertiesVariantTypes.xsdasset7,507 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-math.xsdasset23,313 bytesscripts/office/schemas/ISO-IEC29500-4_2016/shared-relationshipReference.xsdasset1,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/sml.xsdasset242,277 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-main.xsdasset26,148 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-officeDrawing.xsdasset25,279 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-presentationDrawing.xsdasset535 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-spreadsheetDrawing.xsdasset5,712 bytesscripts/office/schemas/ISO-IEC29500-4_2016/vml-wordprocessingDrawing.xsdasset4,010 bytesscripts/office/schemas/ISO-IEC29500-4_2016/wml.xsdasset171,367 bytesscripts/office/schemas/ISO-IEC29500-4_2016/xml.xsdasset4,646 bytesscripts/office/schemas/ecma/fourth-edition/opc-contentTypes.xsdasset1,963 bytesscripts/office/schemas/ecma/fourth-edition/opc-coreProperties.xsdasset2,515 bytesscripts/office/schemas/ecma/fourth-edition/opc-digSig.xsdasset2,856 bytesscripts/office/schemas/ecma/fourth-edition/opc-relationships.xsdasset1,344 bytesscripts/office/schemas/mce/mc.xsdasset3,127 bytesscripts/office/schemas/microsoft/wml-2010.xsdasset26,549 bytesscripts/office/schemas/microsoft/wml-2012.xsdasset3,745 bytesscripts/office/schemas/microsoft/wml-2018.xsdasset901 bytesscripts/office/schemas/microsoft/wml-cex-2018.xsdasset1,778 bytesscripts/office/schemas/microsoft/wml-cid-2016.xsdasset1,002 bytesscripts/office/schemas/microsoft/wml-sdtdatahash-2020.xsdasset600 bytesscripts/office/schemas/microsoft/wml-symex-2015.xsdasset745 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Powerpoint SkillWhen your agent uses it
Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Kheish-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
5,257 tokensWhen the skill is activated
Reference documents
3,728 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use This Skill
- Overview of Attack Modes
- Step 0: Auto-Jailbreak (Recommended)
- Step 1: Choose Your Attack Mode
- Step 2: GODMODE CLASSIC — Quick Start
- Step 3: PARSELTONGUE — Obfuscating Queries
- Step 4: ULTRAPLINIAN — Multi-Model Racing
- Step 5: Detecting Refusals
- Step 6: Advanced — Combining Techniques
- Model-Specific Notes
- Trigger Words (Reference)
- Source Credits
- Tested Results (March 2026)
- Common Pitfalls
Scripts executed separately
scripts/auto_jailbreak.pyscripts/godmode_race.pyscripts/load_godmode.pyscripts/parseltongue.py
Files
SKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesSKILL.mdcore20,733 bytesreferences/jailbreak-templates.mddoc7,429 bytesreferences/refusal-detection.mddoc6,197 bytesscripts/auto_jailbreak.pyruns33,393 bytesscripts/godmode_race.pyruns25,889 bytesscripts/load_godmode.pyruns1,508 bytesscripts/parseltongue.pyruns23,081 bytestemplates/prefill-subtle.jsonasset654 bytestemplates/prefill.jsonasset907 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# G0DM0D3 Jailbreaking SkillWhen your agent uses it
Search and retrieve academic papers from arXiv using their free REST API. No API key needed. Search by keyword, author, category, or ID. Combine with web_extract or the ocr-and-documents skill to read full paper content.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
3,110 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Quick Reference
- Searching Papers
- Search Query Syntax
- Sort and Pagination
- Fetching Specific Papers
- BibTeX Generation
- Reading Paper Content
- Common Categories
- Helper Script
- Semantic Scholar (Citations, Related Papers, Author Profiles)
- Complete Research Workflow
- Rate Limits
- Notes
- ID Versioning
- Withdrawn Papers
Scripts executed separately
scripts/search_arxiv.py
Files
SKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesSKILL.mdcore10,791 bytesscripts/search_arxiv.pyruns4,267 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# arXiv ResearchWhen your agent uses it
Monitor blogs and RSS/Atom feeds for updates using the blogwatcher-cli tool. Add blogs, scan for new articles, track read status, and filter by category.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
1,577 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Installation
- Common Commands
- Environment Variables
- Example Output
- Notes
Files
SKILL.mdcore5,749 bytesSKILL.mdcore5,749 bytesSKILL.mdcore5,749 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# BlogwatcherWhen your agent uses it
Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.
What it loads into context
Frontmatter
148 tokensAlways available to the agent
SKILL.md
4,299 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When This Skill Activates
- Wiki Location
- Architecture: Three Layers
- Resuming an Existing Wiki (CRITICAL — do this every session)
- Initializing a New Wiki
- Tag Taxonomy
- Page Thresholds
- Entity Pages
- Concept Pages
- Comparison Pages
- Update Policy
- Core Operations
- Working with the Wiki
- Pitfalls
Files
SKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytesSKILL.mdcore17,859 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Karpathy's LLM WikiWhen your agent uses it
Query Polymarket prediction market data — search markets, get prices, orderbooks, and price history. Read-only via public REST APIs, no API key needed.
What it loads into context
Frontmatter
62 tokensAlways available to the agent
SKILL.md
869 tokensWhen the skill is activated
Reference documents
1,387 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Key Concepts
- Three Public APIs
- Typical Workflow
- Presenting Results
- Parsing Double-Encoded Fields
- Rate Limits
- Limitations
Scripts executed separately
scripts/polymarket.py
Files
SKILL.mdcore3,577 bytesreferences/api-endpoints.mddoc4,542 bytesscripts/polymarket.pyruns10,208 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Polymarket — Prediction Market DataWhen your agent uses it
End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.
What it loads into context
Frontmatter
253 tokensAlways available to the agent
SKILL.md
24,505 tokensWhen the skill is activated
Reference documents
46,242 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When To Use This Skill
- Core Philosophy
- Phase 0: Project Setup
- Phase 1: Literature Review
- Phase 2: Experiment Design
- Phase 3: Experiment Execution & Monitoring
- Phase 4: Result Analysis
- Iterative Refinement: Strategy Selection
- Phase 5: Paper Drafting
- Phase 6: Self-Review & Revision
- Phase 7: Submission Preparation
- Phase 8: Post-Acceptance Deliverables
- Workshop & Short Papers
- Paper Types Beyond Empirical ML
- Kheish Integration
- Reviewer Evaluation Criteria
- Common Issues and Solutions
- Reference Documents
Scripts executed separately
templates/neurips2025/Makefile
Files
SKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesSKILL.mdcore102,670 bytesreferences/autoreason-methodology.mddoc19,146 bytesreferences/checklists.mddoc13,622 bytesreferences/citation-workflow.mddoc15,433 bytesreferences/experiment-patterns.mddoc25,732 bytesreferences/human-evaluation.mddoc18,221 bytesreferences/paper-types.mddoc16,214 bytesreferences/reviewer-guidelines.mddoc13,738 bytesreferences/sources.mddoc8,772 bytesreferences/writing-guide.mddoc16,292 bytestemplates/README.mddoc6,707 bytestemplates/aaai2026/README.mddoc17,987 bytestemplates/aaai2026/aaai2026-unified-supp.texasset4,548 bytestemplates/aaai2026/aaai2026-unified-template.texasset63,140 bytestemplates/aaai2026/aaai2026.bibasset4,766 bytestemplates/aaai2026/aaai2026.bstasset30,207 bytestemplates/aaai2026/aaai2026.styasset11,802 bytestemplates/acl/README.mddoc2,126 bytestemplates/acl/acl.styasset11,615 bytestemplates/acl/acl_latex.texasset14,533 bytestemplates/acl/acl_lualatex.texasset3,050 bytestemplates/acl/acl_natbib.bstasset45,186 bytestemplates/acl/anthology.bib.txtdoc1,169 bytestemplates/acl/custom.bibasset2,071 bytestemplates/acl/formatting.mddoc17,923 bytestemplates/colm2025/README.mddoc51 bytestemplates/colm2025/colm2025_conference.bibasset496 bytestemplates/colm2025/colm2025_conference.bstasset26,973 bytestemplates/colm2025/colm2025_conference.pdfasset122,635 bytestemplates/colm2025/colm2025_conference.styasset7,727 bytestemplates/colm2025/colm2025_conference.texasset12,830 bytestemplates/colm2025/fancyhdr.styasset20,521 bytestemplates/colm2025/math_commands.texasset12,284 bytestemplates/colm2025/natbib.styasset45,154 bytestemplates/iclr2026/fancyhdr.styasset20,521 bytestemplates/iclr2026/iclr2026_conference.bibasset629 bytestemplates/iclr2026/iclr2026_conference.bstasset26,973 bytestemplates/iclr2026/iclr2026_conference.pdfasset200,508 bytestemplates/iclr2026/iclr2026_conference.styasset9,025 bytestemplates/iclr2026/iclr2026_conference.texasset16,899 bytestemplates/iclr2026/math_commands.texasset12,284 bytestemplates/iclr2026/natbib.styasset45,154 bytestemplates/icml2026/algorithm.styasset2,223 bytestemplates/icml2026/algorithmic.styasset7,414 bytestemplates/icml2026/example_paper.bibasset2,051 bytestemplates/icml2026/example_paper.pdfasset193,509 bytestemplates/icml2026/example_paper.texasset29,714 bytestemplates/icml2026/fancyhdr.styasset31,715 bytestemplates/icml2026/icml2026.bstasset27,147 bytestemplates/icml2026/icml2026.styasset27,344 bytestemplates/icml2026/icml_numpapers.pdfasset2,823 bytestemplates/neurips2025/Makefileruns1,054 bytestemplates/neurips2025/extra_pkgs.texasset2,837 bytestemplates/neurips2025/main.texasset574 bytestemplates/neurips2025/neurips.styasset11,625 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Research Paper Writing PipelineWhen your agent uses it
Control Philips Hue lights, rooms, and scenes via the OpenHue CLI. Turn lights on/off, adjust brightness, color, color temperature, and activate scenes.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
873 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Prerequisites
- When to Use
- Common Commands
- Quick Presets
- Notes
Files
SKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytesSKILL.mdcore3,359 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# OpenHue CLIWhen your agent uses it
Interact with X/Twitter via the x-cli terminal client using official X API credentials. Use for posting, reading timelines, searching tweets, liking, retweeting, bookmarks, mentions, and user lookups.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
1,675 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Important Cost / Access Note
- Install
- Credentials
- Cost / Friction Reality Check
- Where to Store Credentials
- Quick Verification
- Common Commands
- Output Modes
- Agent Workflow
- Pitfalls
- Notes
Files
SKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytesSKILL.mdcore6,702 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Xitter — X/Twitter via x-cliWhen your agent uses it
Plan mode for Kheish — inspect context, write a markdown plan into the active workspace's `.kheish/plans/` directory, and do not execute the work.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
596 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Core behavior
- Output requirements
- Save location
- Interaction style
Files
SKILL.mdcore2,642 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Plan ModeWhen your agent uses it
Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
2,275 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- When to Use
- Step 1 — Get the diff
- Step 2 — Static security scan
- Step 3 — Baseline tests and linting
- Step 4 — Self-review checklist
- Step 5 — Independent reviewer subagent
- Step 6 — Evaluate results
- Step 7 — Auto-fix loop
- Step 8 — Commit
- Reference: Common Patterns to Flag
- Integration with Other Skills
- Pitfalls
Files
SKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytesSKILL.mdcore9,137 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Pre-Commit Code VerificationWhen your agent uses it
Use when executing implementation plans with independent tasks. Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality).
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
2,368 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- When to Use
- The Process
- Task Granularity
- Red Flags — Never Do These
- Handling Issues
- Efficiency Notes
- Integration with Other Skills
- Example Workflow
- Remember
Files
SKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytesSKILL.mdcore10,371 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Subagent-Driven DevelopmentWhen your agent uses it
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
2,592 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- The Iron Law
- When to Use
- The Four Phases
- Phase 1: Root Cause Investigation
- Phase 2: Pattern Analysis
- Phase 3: Hypothesis and Testing
- Phase 4: Implementation
- Red Flags — STOP and Follow Process
- Common Rationalizations
- Quick Reference
- Kheish Integration
- Real-World Impact
Files
SKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytesSKILL.mdcore11,067 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Systematic DebuggingWhen your agent uses it
Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
2,403 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- When to Use
- The Iron Law
- Red-Green-Refactor Cycle
- Why Order Matters
- Common Rationalizations
- Red Flags — STOP and Start Over
- Verification Checklist
- When Stuck
- Kheish Integration
- Testing Anti-Patterns
- Final Rule
Files
SKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytesSKILL.mdcore10,151 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Test-Driven Development (TDD)When your agent uses it
Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
1,901 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Kheish Compatibility
- Overview
- When to Use
- Bite-Sized Task Granularity
- Plan Document Structure
- Writing Process
- Principles
- Common Mistakes
- Execution Handoff
- Remember
Files
SKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytesSKILL.mdcore7,853 bytes
Read SKILL.md
## Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.
# Writing Implementation PlansSkills included
Repository facts
- License
- Apache-2.0
- Created
- Dec 11, 2024
- Last pushed
- Jul 26, 2026
- Forks
- 18
- Open issues
- 0
- Project activity
- Slowing
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 6, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 852 notes
skills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-notes/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-reminders/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-reminders/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-reminders/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-reminders/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/apple-reminders/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/findmy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/findmy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/findmy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/findmy/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/imessage/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/imessage/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/imessage/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/imessage/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/apple/imessage/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- 2 broad permissionsin 2 files: codex/SKILL.md:1 opencode/SKILL.md:1
- 175 dangerous shell commandsin 42 files: linear/SKILL.md:54 research-paper-writing/templates/aaai2026/aaai2026-unified-template.tex:121 unsloth/references/llms-full.md:1029 and 39 more
- 5,162 network referencesin 244 files: unsloth/references/llms-txt.md:35 unsloth/references/llms-full.md:5 axolotl/references/api.md:9 and 241 more
- 5 obfuscation signalsin 3 files: unsloth/references/llms-full.md:556 unsloth/references/llms-txt.md:400 godmode/scripts/parseltongue.py:106
- 11 instruction override signalsin 2 files: claude-code/SKILL.md:113 blogwatcher/SKILL.md:103
- 29 executable scriptsin 29 files: excalidraw/scripts/upload.py:1 manim-video/scripts/setup.sh:1 p5js/scripts/export-frames.js:1 and 26 more
- 433 references to secretsin 49 files: github-repo-management/SKILL.md:50 linear/SKILL.md:9 unsloth/references/llms-full.md:1139 and 46 more
- No requests to vote in Hot
Show all 5,817 signals by file
skills/autonomous-ai-agents/claude-code/SKILL.md
- Network accessline 37
Delegate coding tasks to [Claude Code](https://code.claude.com/docs/en/cli-reference) (Anthropic's autonomous coding agent CLI) via the Kheish terminal. Claude Code v2.x can read files, write code, run shell commands, spawn subagents, and m - Access to secretsline 42
[Credential-related source omitted] - Instruction override signalline 113
### Dialog 2: Bypass Permissions Warning (only with --dangerously-skip-permissions) - Instruction override signalline 125
# Launch with permissions bypass terminal(command="tmux send-keys -t claude-work 'claude --dangerously-skip-permissions \"your task\"' Enter") - Instruction override signalline 126
terminal(command="tmux send-keys -t claude-work 'claude --dangerously-skip-permissions \"your task\"' Enter") - Instruction override signalline 138
**Note:** After the first trust acceptance for a directory, the trust dialog won't appear again. Only the permissions dialog recurs each time you use `--dangerously-skip-permissions`. - Access to secretsline 250
[Credential-related source omitted] - Instruction override signalline 297
|------|--------| | `--dangerously-skip-permissions` | Auto-approve ALL tool use (file writes, bash, network, etc.) | | `--allow-dangerously-skip-permissions` | Enable bypass as an *option* without enabling it by default | - Instruction override signalline 298
| `--dangerously-skip-permissions` | Auto-approve ALL tool use (file writes, bash, network, etc.) | - Dangerous shell operationline 371
"deny": ["Read(.env)", "Bash(rm -rf *)"] - Dangerous shell operationline 620
"hooks": [{"type": "command", "command": "if echo \"$CLAUDE_TOOL_INPUT\" | grep -q 'rm -rf'; then echo 'Blocked!' && exit 2; fi"}] - Dangerous shell operationline 653
"hooks": [{"type": "command", "command": "if echo \"$CLAUDE_TOOL_INPUT\" | grep -qE 'rm -rf|git push.*--force|:(){ :|:& };:'; then echo 'Dangerous command blocked!' && exit 2; fi"}] - Access to secretsline 719
[Credential-related source omitted] - Instruction override signalline 742
1. **Interactive mode REQUIRES tmux** — Claude Code is a full TUI app. Using `pty=true` alone in Kheish terminal works but tmux gives you `capture-pane` for monitoring and `send-keys` for input, which is essential for orchestration. 2. **`- - Instruction override signalline 743
2. **`--dangerously-skip-permissions` dialog defaults to "No, exit"** — you must send Down then Enter to accept. Print mode (`-p`) skips this entirely. - Access to secretsline 752
[Credential-related source omitted]
skills/autonomous-ai-agents/codex/SKILL.md
- Broad permissionsline 1
Unsupported or unresolved allowed-tools declaration - Network accessline 34
Delegate coding tasks to [Codex](https://github.com/openai/codex) via the Kheish terminal. Codex is OpenAI's autonomous coding agent CLI. - Network accessline 82
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && codex review --base origin/main", pty=true)
skills/autonomous-ai-agents/opencode/SKILL.md
- Broad permissionsline 1
Unsupported or unresolved allowed-tools declaration - Network accessline 35
Use [OpenCode](https://opencode.ai) as an autonomous coding worker orchestrated by Kheish terminal/process tools. OpenCode is a provider-agnostic, open-source AI coding agent with a TUI and CLI. - Access to secretsline 47
[Credential-related source omitted] - Network accessline 180
terminal(command="REVIEW=$(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 86
# Basic text banner (default font) curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World" - Network accessline 87
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello+World" - Network accessline 89
# With a specific font curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant" - Network accessline 90
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Slant" - Network accessline 91
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Doom" - Network accessline 92
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Star+Wars" - Network accessline 93
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=3-D" - Network accessline 94
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=Hello&font=Banner3" - Network accessline 96
# List all available fonts (returns JSON array) curl -s "https://asciified.thelicato.io/api/v2/fonts" - Network accessline 97
curl -s "https://asciified.thelicato.io/api/v2/fonts" - Network accessline 103
- 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 105
- Works from any terminal with curl — no Python or pip needed - Dangerous shell operationline 114
sudo apt install cowsay -y # Debian/Ubuntu - Dangerous shell operationline 158
sudo apt install boxes -y # Debian/Ubuntu - Network accessline 181
python3 -m pyfiglet "HERMES" -f slant | boxes -d stone # Or without pyfiglet installed: curl -s "https://asciified.thelicato.io/api/v2/ascii?text=HERMES&font=Slant" | boxes -d stone - Network accessline 183
curl -s "https://asciified.thelicato.io/api/v2/ascii?text=HERMES&font=Slant" | boxes -d stone - Dangerous shell operationline 193
sudo apt install toilet toilet-fonts -y # Debian/Ubuntu - Dangerous shell operationline 224
sudo snap install ascii-image-converter - Network accessline 232
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 233
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 234
ascii-image-converter https://url/image.jpg # Direct URL - Dangerous shell operationline 241
sudo apt install jp2a -y - Network accessline 254
**URL pattern:** `https://ascii.co.uk/art/{subject}` - Network accessline 259
curl -s 'https://ascii.co.uk/art/cat' -o /tmp/ascii_art.html - Network accessline 294
curl -s https://api.github.com/octocat - Network accessline 304
curl -s "qrenco.de/Hello+World" - Network accessline 305
curl -s "qrenco.de/https://example.com" - Network accessline 311
curl -s "wttr.in/London" # Full weather report with ASCII graphics - Network accessline 312
curl -s "wttr.in/Moon" # Moon phase in ASCII art - Network accessline 313
curl -s "v2.wttr.in/London" # Detailed version - Network accessline 336
1. **Text as a banner** → pyfiglet if installed, otherwise asciified API via curl 2. **Wrap a message in fun character art** → cowsay - Network accessline 337
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 338
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 339
4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing - Network accessline 340
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 341
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 342
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/references/inputs.md
- Access to secretsline 301
[Credential-related source omitted] - Network accessline 307
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" - Access to secretsline 308
[Credential-related source omitted] - Network accessline 317
}, } resp = requests.post(url, json=data, headers=headers, stream=True) - Network accessline 318
} resp = requests.post(url, json=data, headers=headers, stream=True) resp.raise_for_status() - Network accessline 319
resp = requests.post(url, json=data, headers=headers, stream=True)
skills/creative/ascii-video/references/shaders.md
skills/creative/creative-ideation/SKILL.md
- Network accessline 167
Constraint approach inspired by [wttdotm.com/prompts.html](https://wttdotm.com/prompts.html). Adapted and expanded for software development and general-purpose ideation.
skills/creative/excalidraw/SKILL.md
- Network accessline 36
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/creative/manim-video/README.md
- Network accessline 3
Production pipeline for mathematical and technical animations using [Manim Community Edition](https://www.manim.community/).
skills/creative/manim-video/scripts/setup.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/creative/p5js/README.md
- Network accessline 3
Production pipeline for interactive and generative visual art using [p5.js](https://p5js.org/).
skills/creative/p5js/SKILL.md
- Network accessline 116
|-----------|---------|-----------| | **Color system** | HSB/HSL, RGB, named palettes, procedural harmony, gradient interpolation | `references/color-systems.md` | | **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain wa - Network accessline 117
| **Color system** | HSB/HSL, RGB, named palettes, procedural harmony, gradient interpolation | `references/color-systems.md` | | **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain warping, curl noise | `references/visu - Network accessline 118
| **Noise vocabulary** | Perlin noise, simplex, fractal (octaved), domain warping, curl noise | `references/visual-effects.md` § Noise | - Network accessline 141
For every project, invent at least one of: - A custom color palette matching the mood (not a preset) - A novel noise field combination (e.g., curl noise + domain warp + feedback) - Network accessline 142
- A custom color palette matching the mood (not a preset) - A novel noise field combination (e.g., curl noise + domain warp + feedback) - A unique particle behavior (custom forces, custom trails, custom spawning) - Network accessline 143
- A novel noise field combination (e.g., curl noise + domain warp + feedback) - Network accessline 205
<title>Project Name</title> <script>p5.disableFriendlyErrors = true;</script> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 206
<script>p5.disableFriendlyErrors = true;</script> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> <!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></scri - Network accessline 207
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 208
<!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></script> --> - Network accessline 209
<!-- <script src="https://unpkg.com/p5.js-svg@1.6.0"></script> --> <!-- SVG export --> - Network accessline 210
<!-- <script src="https://cdn.jsdelivr.net/npm/ccapture.js-npmfixed/build/CCapture.all.min.js"></script> --> <!-- video capture --> - Network accessline 510
1. **Write the HTML file** — single self-contained file, all code inline 2. **Open in browser** — `open sketch.html` (macOS) or `xdg-open sketch.html` (Linux) 3. **Local assets** (fonts, images) require a server: `python3 -m http.server 808 - Network accessline 511
2. **Open in browser** — `open sketch.html` (macOS) or `xdg-open sketch.html` (Linux) 3. **Local assets** (fonts, images) require a server: `python3 -m http.server 8080` in the project directory, then open `http://localhost:8080/sketch.html - Network accessline 512
3. **Local assets** (fonts, images) require a server: `python3 -m http.server 8080` in the project directory, then open `http://localhost:8080/sketch.html`
skills/creative/p5js/references/core-api.md
- Network accessline 409
<script src="https://cdn.jsdelivr.net/npm/p5@2/lib/p5.min.js"></script>
skills/creative/p5js/references/export-pipeline.md
skills/creative/p5js/references/interaction.md
- Network accessline 276
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/addons/p5.sound.min.js"></script>
skills/creative/p5js/references/typography.md
- Network accessline 31
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;700&display=swap" rel="stylesheet">
skills/creative/p5js/references/visual-effects.md
- Network accessline 66
### Curl Noise - Network accessline 68
Divergence-free noise field. Particles following curl noise never converge or diverge — they flow in smooth, swirling patterns. - Network accessline 76
let dndy = (noise(x * scale, y * scale + eps, time) - noise(x * scale, y * scale - eps, time)) / (2 * eps); // Curl = perpendicular to gradient - Network accessline 77
noise(x * scale, y * scale - eps, time)) / (2 * eps); // Curl = perpendicular to gradient return createVector(dndy, -dndx); - Network accessline 78
// Curl = perpendicular to gradient - Network accessline 794
for (let dy = -2; dy <= 2; dy++) { for (let dx = -2; dx <= 2; dx++) { let nc = col + dx, nr = row + dy; - Network accessline 795
for (let dx = -2; dx <= 2; dx++) { let nc = col + dx, nr = row + dy; if (nc >= 0 && nc < cols && nr >= 0 && nr < rows) { - Network accessline 796
let nc = col + dx, nr = row + dy; - Network accessline 797
if (nc >= 0 && nc < cols && nr >= 0 && nr < rows) { - Network accessline 798
let gi = nc + nr * cols; - Network accessline 825
<script src="https://cdn.jsdelivr.net/npm/p5.brush@latest/dist/p5.brush.js"></script> - Network accessline 849
<script src="https://cdn.jsdelivr.net/npm/p5.grain@0.7.0/p5.grain.min.js"></script> - Network accessline 865
<script src="https://cdn.jsdelivr.net/npm/ccapture.js-npmfixed/build/CCapture.all.min.js"></script>
skills/creative/p5js/scripts/export-frames.js
- Executable scriptline 1
#!/usr/bin/env node
skills/creative/p5js/scripts/render.sh
skills/creative/p5js/scripts/serve.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 16
echo "=== p5.js Dev Server ===" echo "Serving: $(cd "$DIR" && pwd)" echo "URL: http://localhost:$PORT" - Network accessline 17
echo "Serving: $(cd "$DIR" && pwd)" echo "URL: http://localhost:$PORT" echo "Press Ctrl+C to stop" - Network accessline 18
echo "URL: http://localhost:$PORT"
skills/creative/p5js/scripts/setup.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 23
else warn "Node.js not found — optional, needed for headless export" echo " Install: https://nodejs.org/ or 'brew install node'" - Network accessline 24
warn "Node.js not found — optional, needed for headless export" echo " Install: https://nodejs.org/ or 'brew install node'" fi - Network accessline 25
echo " Install: https://nodejs.org/ or 'brew install node'"
skills/creative/p5js/templates/viewer.html
- Network accessline 26
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Generative Art Viewer</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> - Network accessline 27
<title>Generative Art Viewer</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script> <style> - Network accessline 28
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.11.3/p5.min.js"></script>
skills/creative/popular-web-designs/SKILL.md
- Network accessline 69
<title>Page Title</title> <!-- Paste the Google Fonts <link> from the template's Kheish notes --> <link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet"> - Network accessline 70
<!-- Paste the Google Fonts <link> from the template's Kheish notes --> <link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet"> <style> - Network accessline 71
<link href="https://fonts.googleapis.com/css2?family=..." rel="stylesheet">
skills/creative/popular-web-designs/templates/airbnb.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/airtable.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/bmw.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cal.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Roboto Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Roboto+Mono:wght@400;500;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Roboto+Mono:wght@400;500;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/claude.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/clay.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/clickhouse.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cohere.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/coinbase.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/composio.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,ops - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/cursor.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/elevenlabs.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/expo.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/figma.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/framer.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Azeret Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Azeret+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Azeret+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/hashicorp.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/ibm.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'IBM Plex Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wgh - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/intercom.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/kraken.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
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> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/linear.app.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/lovable.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/minimax.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mintlify.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/miro.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mistral.ai.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/mongodb.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500;600&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500;600&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/notion.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
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> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/nvidia.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/ollama.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/opencode.ai.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:w - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/pinterest.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/posthog.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/raycast.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/replicate.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/resend.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/revolut.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/runwayml.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/sanity.md
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> - **Mono stack (CSS):** `font-family: 'IBM Plex Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wgh - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;400;500;600;700&family=IBM+Plex+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;400;500;600;700&family=IBM+Plex+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/sentry.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Rubik:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/spacex.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
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> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/spotify.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/stripe.md
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> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:w - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:wght@300;400;500;600&family=Source+Code+Pro:wght@400;500;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/supabase.md
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> - **Mono stack (CSS):** `font-family: 'Source Code Pro', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Source+Code+Pro:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/superhuman.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/together.ai.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;4 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/uber.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100 - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&display=swap" rel="stylesheet"> > ``` - Network accessline 11
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skills/creative/popular-web-designs/templates/vercel.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
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skills/creative/popular-web-designs/templates/voltagent.md
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> - **Mono stack (CSS):** `font-family: 'JetBrains Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:w - Network accessline 10
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skills/creative/popular-web-designs/templates/warp.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;5 - Network accessline 10
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> <link href="https://fonts.googleapis.com/css2?family=Geist:wght@300;400;500;600&family=Geist+Mono:wght@400;500&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/webflow.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
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skills/creative/popular-web-designs/templates/wise.md
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> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
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skills/creative/popular-web-designs/templates/x.ai.md
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> - **Mono stack (CSS):** `font-family: 'Geist Mono', ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300; - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Geist+Mono:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/creative/popular-web-designs/templates/zapier.md
- Network accessline 9
> - **Mono stack (CSS):** `font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace;` > ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&dis - Network accessline 10
> ```html > <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> > ``` - Network accessline 11
> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
skills/data-science/jupyter-live-kernel/SKILL.md
skills/devops/webhook-subscriptions/SKILL.md
- Network accessline 79
curl http://localhost:8644/health - Network accessline 196
1. **Is the gateway running?** Check with `systemctl --user status kheish-daemon` or `ps aux | grep gateway` 2. **Is the webhook server listening?** `curl http://localhost:8644/health` should return `{"status": "ok"}` - Network accessline 197
2. **Is the webhook server listening?** `curl http://localhost:8644/health` should return `{"status": "ok"}`
skills/dogfood/SKILL.md
- Network accessline 73
browser_navigate(url="https://example.com/page")
skills/email/himalaya/SKILL.md
- Network accessline 14
- CLI - Communication homepage: https://github.com/pimalaya/himalaya - Network accessline 15
- Communication homepage: https://github.com/pimalaya/himalaya prerequisites: - Network accessline 16
homepage: https://github.com/pimalaya/himalaya - Network accessline 53
# Pre-built binary (Linux/macOS — recommended) curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh - Network accessline 54
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 54
mkdir -p ~/minecraft-server cd ~/minecraft-server wget -O serverpack.zip "<URL>" - Network accessline 55
cd ~/minecraft-server wget -O serverpack.zip "<URL>" unzip -o serverpack.zip -d server - Network accessline 56
wget -O serverpack.zip "<URL>" - Dangerous shell operationline 64
- Minecraft 1.21+ → Java 21: `sudo apt install openjdk-21-jre-headless` - Dangerous shell operationline 65
- Minecraft 1.18-1.20 → Java 17: `sudo apt install openjdk-17-jre-headless` - Dangerous shell operationline 66
- Minecraft 1.16 and below → Java 8: `sudo apt install openjdk-8-jre-headless` - Dangerous shell operationline 144
sudo ufw allow 25565/tcp comment "Minecraft Server" - Dangerous shell operationline 146
Check with: `sudo ufw status | grep 25565` - Dangerous shell operationline 179
ls -1t "$BACKUP_DIR"/world_*.tar.gz | tail -n "$REMOVE" | xargs rm -f
skills/github/github-auth/SKILL.md
- Dangerous shell operationline 60
3. If `gh` is not installed → use "git-only" method below (no sudo needed) - Network accessline 74
Tell the user to go to: **https://github.com/settings/tokens** - Access to secretsline 89
[Credential-related source omitted] - Network accessline 93
[Credential-related source omitted] - Network accessline 95
git ls-remote https://github.com/<their-username>/<any-repo>.git - Network accessline 125
# Test push access (this should work without any prompts now) git ls-remote https://github.com/<their-username>/<any-repo>.git - Network accessline 126
git ls-remote https://github.com/<their-username>/<any-repo>.git - Access to secretsline 140
[Credential-related source omitted] - Access to secretsline 147
[Credential-related source omitted] - Access to secretsline 150
[Credential-related source omitted] - Network accessline 153
Tell the user to add the public key at: **https://github.com/settings/keys** - Network accessline 168
# Rewrite HTTPS GitHub URLs to SSH automatically git config --global url."git@github.com:".insteadOf "https://github.com/" - Network accessline 169
git config --global url."git@github.com:".insteadOf "https://github.com/" - Access to secretsline 219
[Credential-related source omitted] - Network accessline 221
# Then use in curl calls: - Network accessline 222
[Credential-related source omitted] - Access to secretsline 222
[Credential-related source omitted] - Network accessline 223
https://api.github.com/user - Access to secretsline 233
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Access to secretsline 260
[Credential-related source omitted] - Access to secretsline 262
[Credential-related source omitted] - Dangerous shell operationline 263
| `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
[ "$GH_AUTH_METHOD" = "none" ] && echo "⚠ Not authenticated — see github-auth skill"
skills/github/github-code-review/SKILL.md
- Access to secretsline 47
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 159
# Get PR details curl -s \ - Dangerous shell operationline 160
[Credential-related source omitted] - Network accessline 160
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Network accessline 161
[Credential-related source omitted] - Access to secretsline 161
[Credential-related source omitted] - Network accessline 162
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 172
# List changed files curl -s \ - Dangerous shell operationline 173
[Credential-related source omitted] - Network accessline 173
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Network accessline 174
[Credential-related source omitted] - Access to secretsline 174
[Credential-related source omitted] - Network accessline 175
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/files \ - Network accessline 214
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Network accessline 216
https://api.github.com/repos/$OWNER/$REPO/issues/$PR_NUMBER/comments \ - Network accessline 239
# Get the head commit SHA HEAD_SHA=$(curl -s \ - Dangerous shell operationline 240
[Credential-related source omitted] - Network accessline 240
[Credential-related source omitted] - Access to secretsline 240
[Credential-related source omitted] - Network accessline 241
[Credential-related source omitted] - Access to secretsline 241
[Credential-related source omitted] - Network accessline 242
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 245
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Network accessline 246
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 247
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/comments \ - Network accessline 267
**With curl — multi-comment review submitted atomically:** - Dangerous shell operationline 270
[Credential-related source omitted] - Network accessline 270
[Credential-related source omitted] - Access to secretsline 270
[Credential-related source omitted] - Network accessline 271
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Network accessline 272
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - Network accessline 275
[Credential-related source omitted] - Access to secretsline 275
[Credential-related source omitted] - Network accessline 276
[Credential-related source omitted] - Access to secretsline 276
[Credential-related source omitted] - Network accessline 277
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/reviews \ - Network accessline 373
[Credential-related source omitted] - Network accessline 374
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Network accessline 375
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER - Network accessline 377
[Credential-related source omitted] - Network accessline 378
[Credential-related source omitted] - Access to secretsline 378
[Credential-related source omitted] - Network accessline 379
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/files - Network accessline 434
**With curl — atomic review with multiple inline comments:** - Dangerous shell operationline 436
[Credential-related source omitted] - Network accessline 436
[Credential-related source omitted] - Access to secretsline 436
[Credential-related source omitted] - Network accessline 437
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER \ - Network accessline 440
# Build the review JSON — event is APPROVE, REQUEST_CHANGES, or COMMENT curl -s -X POST \ - Network accessline 441
[Credential-related source omitted] - Access to secretsline 441
[Credential-related source omitted] - Network accessline 442
[Credential-related source omitted] - Access to secretsline 442
[Credential-related source omitted] - Network accessline 443
https://api.github.com/repos/$GH_OWNER/$GH_REPO/pulls/$PR_NUMBER/reviews \
skills/github/github-issues/SKILL.md
- Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 50
[Credential-related source omitted] - Network accessline 51
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 53
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Network accessline 81
# List open issues curl -s \ - Dangerous shell operationline 82
[Credential-related source omitted] - Network accessline 82
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Network accessline 83
[Credential-related source omitted] - Access to secretsline 83
[Credential-related source omitted] - Network accessline 84
"https://api.github.com/repos/$OWNER/$REPO/issues?state=open&per_page=20" \ - Network accessline 92
# Filter by label curl -s \ - Dangerous shell operationline 93
[Credential-related source omitted] - Network accessline 93
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Network accessline 94
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Network accessline 95
"https://api.github.com/repos/$OWNER/$REPO/issues?state=open&labels=bug&per_page=20" \ - Network accessline 102
# View a specific issue curl -s \ - Dangerous shell operationline 103
[Credential-related source omitted] - Network accessline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Network accessline 104
[Credential-related source omitted] - Access to secretsline 104
[Credential-related source omitted] - Network accessline 105
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 116
# Search issues curl -s \ - Dangerous shell operationline 117
[Credential-related source omitted] - Network accessline 117
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Network accessline 118
[Credential-related source omitted] - Access to secretsline 118
[Credential-related source omitted] - Network accessline 119
"https://api.github.com/search/issues?q=authentication+error+repo:$OWNER/$REPO" \ - Network accessline 151
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Network accessline 152
[Credential-related source omitted] - Access to secretsline 152
[Credential-related source omitted] - Network accessline 153
https://api.github.com/repos/$OWNER/$REPO/issues \ - Network accessline 213
# Add labels curl -s -X POST \ - Network accessline 214
[Credential-related source omitted] - Access to secretsline 214
[Credential-related source omitted] - Network accessline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Network accessline 216
https://api.github.com/repos/$OWNER/$REPO/issues/42/labels \ - Network accessline 219
# Remove a label curl -s -X DELETE \ - Network accessline 220
[Credential-related source omitted] - Access to secretsline 220
[Credential-related source omitted] - Access to secretsline 221
[Credential-related source omitted] - Network accessline 222
https://api.github.com/repos/$OWNER/$REPO/issues/42/labels/needs-triage - Network accessline 224
# List available labels in the repo curl -s \ - Dangerous shell operationline 225
[Credential-related source omitted] - Network accessline 225
[Credential-related source omitted] - Access to secretsline 225
[Credential-related source omitted] - Network accessline 226
[Credential-related source omitted] - Access to secretsline 226
[Credential-related source omitted] - Network accessline 227
https://api.github.com/repos/$OWNER/$REPO/labels \ - Network accessline 246
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 247
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Network accessline 248
https://api.github.com/repos/$OWNER/$REPO/issues/42/assignees \ - Network accessline 263
[Credential-related source omitted] - Access to secretsline 263
[Credential-related source omitted] - Network accessline 264
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Network accessline 265
https://api.github.com/repos/$OWNER/$REPO/issues/42/comments \ - Network accessline 282
# Close curl -s -X PATCH \ - Network accessline 283
[Credential-related source omitted] - Access to secretsline 283
[Credential-related source omitted] - Network accessline 284
[Credential-related source omitted] - Access to secretsline 284
[Credential-related source omitted] - Network accessline 285
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 288
# Reopen curl -s -X PATCH \ - Network accessline 289
[Credential-related source omitted] - Access to secretsline 289
[Credential-related source omitted] - Network accessline 290
[Credential-related source omitted] - Access to secretsline 290
[Credential-related source omitted] - Network accessline 291
https://api.github.com/repos/$OWNER/$REPO/issues/42 \ - Network accessline 330
# With curl curl -s \ - Dangerous shell operationline 331
[Credential-related source omitted] - Network accessline 331
[Credential-related source omitted] - Access to secretsline 331
[Credential-related source omitted] - Network accessline 332
[Credential-related source omitted] - Access to secretsline 332
[Credential-related source omitted] - Network accessline 333
"https://api.github.com/repos/$OWNER/$REPO/issues?labels=needs-triage&state=open" \ - Network accessline 364
# List issue numbers with a label, then close each curl -s \ - Dangerous shell operationline 365
[Credential-related source omitted] - Network accessline 365
[Credential-related source omitted] - Access to secretsline 365
[Credential-related source omitted] - Network accessline 366
[Credential-related source omitted] - Access to secretsline 366
[Credential-related source omitted] - Network accessline 367
"https://api.github.com/repos/$OWNER/$REPO/issues?labels=wontfix&state=open" \ - Network accessline 368
| python3 -c "import sys,json; [print(i['number']) for i in json.load(sys.stdin)]" \ | while read num; do curl -s -X PATCH \ - Network accessline 369
[Credential-related source omitted] - Network accessline 370
[Credential-related source omitted] - Access to secretsline 370
[Credential-related source omitted] - Access to secretsline 371
[Credential-related source omitted] - Network accessline 372
https://api.github.com/repos/$OWNER/$REPO/issues/$num \ - Network accessline 380
| Action | gh | curl endpoint |
skills/github/github-pr-workflow/SKILL.md
- Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Network accessline 151
[Credential-related source omitted] - Access to secretsline 151
[Credential-related source omitted] - Network accessline 152
[Credential-related source omitted] - Access to secretsline 152
[Credential-related source omitted] - Network accessline 153
-H "Accept: application/vnd.github.v3+json" \ https://api.github.com/repos/$OWNER/$REPO/pulls \ -d "{ - Network accessline 154
https://api.github.com/repos/$OWNER/$REPO/pulls \ - Network accessline 187
# Query the combined status curl -s \ - Dangerous shell operationline 188
[Credential-related source omitted] - Network accessline 188
[Credential-related source omitted] - Access to secretsline 188
[Credential-related source omitted] - Network accessline 189
[Credential-related source omitted] - Access to secretsline 189
[Credential-related source omitted] - Network accessline 190
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \ - Network accessline 198
# Also check GitHub Actions check runs (separate endpoint) curl -s \ - Dangerous shell operationline 199
[Credential-related source omitted] - Network accessline 199
[Credential-related source omitted] - Access to secretsline 199
[Credential-related source omitted] - Network accessline 200
[Credential-related source omitted] - Access to secretsline 200
[Credential-related source omitted] - Network accessline 201
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \ - Network accessline 213
SHA=$(git rev-parse HEAD) for i in $(seq 1 20); do STATUS=$(curl -s \ - Network accessline 214
[Credential-related source omitted] - Dangerous shell operationline 215
[Credential-related source omitted] - Network accessline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 216
[Credential-related source omitted] - Network accessline 217
https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \ - Network accessline 248
# List workflow runs on this branch curl -s \ - Dangerous shell operationline 249
[Credential-related source omitted] - Network accessline 249
[Credential-related source omitted] - Access to secretsline 249
[Credential-related source omitted] - Network accessline 250
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 251
"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5" \ - Network accessline 258
# Get failed job logs (download as zip, extract, read) RUN_ID=<run_id> curl -s -L \ - Network accessline 259
[Credential-related source omitted] - Network accessline 260
[Credential-related source omitted] - Access to secretsline 260
[Credential-related source omitted] - Network accessline 261
[Credential-related source omitted] - Access to secretsline 261
[Credential-related source omitted] - Network accessline 262
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \ - Network accessline 309
# Merge the PR via API (squash) curl -s -X PUT \ - Network accessline 310
[Credential-related source omitted] - Access to secretsline 310
[Credential-related source omitted] - Network accessline 311
[Credential-related source omitted] - Access to secretsline 311
[Credential-related source omitted] - Network accessline 312
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \ - Network accessline 332
# 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=$(curl -s \ - Network accessline 333
[Credential-related source omitted] - Dangerous shell operationline 334
[Credential-related source omitted] - Network accessline 334
[Credential-related source omitted] - Access to secretsline 334
[Credential-related source omitted] - Network accessline 335
[Credential-related source omitted] - Access to secretsline 335
[Credential-related source omitted] - Network accessline 336
https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \ - 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 341
https://api.github.com/graphql \ - Network accessline 365
# 6. Create PR (picks gh or curl based on what's available) - Network accessline 375
| Action | gh | git + curl | - Network accessline 376
[Credential-related source omitted] - Network accessline 377
[Credential-related source omitted] - Access to secretsline 377
[Credential-related source omitted] - Network accessline 378
| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H "Accept: application/vnd.github.diff" ...` | - Network accessline 379
| Add comment | `gh pr comment N --body "..."` | `curl -X POST .../issues/N/comments -d '{"body":"..."}'` | - Network accessline 380
| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{"reviewers":["user"]}'` | - Network accessline 381
| 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
skills/github/github-repo-management/SKILL.md
- Access to secretsline 50
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 53
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted] - Network accessline 54
[Credential-related source omitted] - Access to secretsline 54
[Credential-related source omitted] - Network accessline 61
[Credential-related source omitted] - Network accessline 62
[Credential-related source omitted] - Dangerous shell operationline 63
[Credential-related source omitted] - Network accessline 63
[Credential-related source omitted] - Access to secretsline 63
[Credential-related source omitted] - Network accessline 83
# Clone via HTTPS (works with credential helper or token-embedded URL) git clone https://github.com/owner/repo-name.git - Network accessline 84
git clone https://github.com/owner/repo-name.git - Network accessline 86
# Clone into a specific directory git clone https://github.com/owner/repo-name.git ./my-local-dir - Network accessline 87
git clone https://github.com/owner/repo-name.git ./my-local-dir - Network accessline 89
# Shallow clone (faster for large repos) git clone --depth 1 https://github.com/owner/repo-name.git - Network accessline 90
git clone --depth 1 https://github.com/owner/repo-name.git - Network accessline 92
# Clone a specific branch git clone --branch develop https://github.com/owner/repo-name.git - Network accessline 93
git clone --branch develop https://github.com/owner/repo-name.git - Network accessline 128
# Create the remote repo via API curl -s -X POST \ - Network accessline 129
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Network accessline 130
[Credential-related source omitted] - Access to secretsline 130
[Credential-related source omitted] - Network accessline 131
https://api.github.com/user/repos \ - Network accessline 140
# Clone it git clone https://github.com/$GH_USER/my-new-project.git - Network accessline 141
git clone https://github.com/$GH_USER/my-new-project.git - Network accessline 147
git add . git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git - Network accessline 148
git commit -m "Initial commit" git remote add origin https://github.com/$GH_USER/my-new-project.git git push -u origin main - Network accessline 149
git remote add origin https://github.com/$GH_USER/my-new-project.git - Network accessline 156
[Credential-related source omitted] - Access to secretsline 156
[Credential-related source omitted] - Network accessline 157
[Credential-related source omitted] - Access to secretsline 157
[Credential-related source omitted] - Network accessline 158
https://api.github.com/orgs/my-org/repos \ - Network accessline 173
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Network accessline 174
[Credential-related source omitted] - Access to secretsline 174
[Credential-related source omitted] - Network accessline 175
https://api.github.com/repos/owner/template-repo/generate \ - Network accessline 190
# Create the fork via API curl -s -X POST \ - Network accessline 191
[Credential-related source omitted] - Access to secretsline 191
[Credential-related source omitted] - Access to secretsline 192
[Credential-related source omitted] - Network accessline 193
https://api.github.com/repos/owner/repo-name/forks - Network accessline 195
# Wait a moment for GitHub to create it, then clone sleep 3 git clone https://github.com/$GH_USER/repo-name.git - Network accessline 196
sleep 3 git clone https://github.com/$GH_USER/repo-name.git cd repo-name - Network accessline 197
git clone https://github.com/$GH_USER/repo-name.git - Network accessline 200
# Add the original repo as "upstream" remote git remote add upstream https://github.com/owner/repo-name.git - Network accessline 201
git remote add upstream https://github.com/owner/repo-name.git - Network accessline 233
# View repo details curl -s \ - Dangerous shell operationline 234
[Credential-related source omitted] - Network accessline 234
[Credential-related source omitted] - Access to secretsline 234
[Credential-related source omitted] - Network accessline 235
[Credential-related source omitted] - Access to secretsline 235
[Credential-related source omitted] - Network accessline 236
https://api.github.com/repos/$OWNER/$REPO \ - Network accessline 246
# List your repos curl -s \ - Dangerous shell operationline 247
[Credential-related source omitted] - Network accessline 247
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Network accessline 248
[Credential-related source omitted] - Access to secretsline 248
[Credential-related source omitted] - Network accessline 249
"https://api.github.com/user/repos?per_page=20&sort=updated" \ - Network accessline 256
# Search repos curl -s \ - Dangerous shell operationline 257
curl -s "https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10" | python3 -c " - Network accessline 257
curl -s \ - Network accessline 258
"https://api.github.com/search/repositories?q=machine+learning+language:python&sort=stars&per_page=10" \ - Network accessline 280
[Credential-related source omitted] - Access to secretsline 280
[Credential-related source omitted] - Network accessline 281
[Credential-related source omitted] - Access to secretsline 281
[Credential-related source omitted] - Network accessline 282
https://api.github.com/repos/$OWNER/$REPO \ - Network accessline 290
# Update topics curl -s -X PUT \ - Network accessline 291
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Network accessline 292
[Credential-related source omitted] - Access to secretsline 292
[Credential-related source omitted] - Network accessline 293
-H "Accept: application/vnd.github.mercy-preview+json" \ https://api.github.com/repos/$OWNER/$REPO/topics \ -d '{"names": ["machine-learning", "python", "automation"]}' - Network accessline 294
https://api.github.com/repos/$OWNER/$REPO/topics \ - Network accessline 301
# View current protection curl -s \ - Network accessline 302
[Credential-related source omitted] - Access to secretsline 302
[Credential-related source omitted] - Access to secretsline 303
[Credential-related source omitted] - Network accessline 304
https://api.github.com/repos/$OWNER/$REPO/branches/main/protection - Network accessline 306
# Set up branch protection curl -s -X PUT \ - 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/branches/main/protection \ - Access to secretsline 328
[Credential-related source omitted] - Access to secretsline 329
[Credential-related source omitted] - Access to secretsline 331
[Credential-related source omitted] - Network accessline 339
# Get the repo's public key for encrypting secrets curl -s \ - Network accessline 340
[Credential-related source omitted] - Access to secretsline 340
[Credential-related source omitted] - Access to secretsline 341
[Credential-related source omitted] - Network accessline 342
https://api.github.com/repos/$OWNER/$REPO/actions/secrets/public-key - Network accessline 363
# Then PUT the encrypted secret curl -s -X PUT \ - Network accessline 364
[Credential-related source omitted] - Access to secretsline 364
[Credential-related source omitted] - Network accessline 365
[Credential-related source omitted] - Access to secretsline 365
[Credential-related source omitted] - Network accessline 366
[Credential-related source omitted] - Access to secretsline 366
[Credential-related source omitted] - Network accessline 369
# List secrets (names only, values hidden) curl -s \ - Dangerous shell operationline 370
[Credential-related source omitted] - Network accessline 370
[Credential-related source omitted] - Access to secretsline 370
[Credential-related source omitted] - Network accessline 371
[Credential-related source omitted] - Access to secretsline 371
[Credential-related source omitted] - Network accessline 372
https://api.github.com/repos/$OWNER/$REPO/actions/secrets \ - Network accessline 396
# Create a release curl -s -X POST \ - Network accessline 397
[Credential-related source omitted] - Access to secretsline 397
[Credential-related source omitted] - Network accessline 398
[Credential-related source omitted] - Access to secretsline 398
[Credential-related source omitted] - Network accessline 399
https://api.github.com/repos/$OWNER/$REPO/releases \ - Network accessline 409
# List releases curl -s \ - Dangerous shell operationline 410
[Credential-related source omitted] - Network accessline 410
[Credential-related source omitted] - Access to secretsline 410
[Credential-related source omitted] - Network accessline 411
[Credential-related source omitted] - Access to secretsline 411
[Credential-related source omitted] - Network accessline 412
https://api.github.com/repos/$OWNER/$REPO/releases \ - Network accessline 419
# Upload a release asset (binary file) RELEASE_ID=<id_from_create_response> curl -s -X POST \ - Network accessline 420
[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
-H "Content-Type: application/octet-stream" \ "https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \ --data-binary @./dist/binary-amd64 - Network accessline 424
"https://uploads.github.com/repos/$OWNER/$REPO/releases/$RELEASE_ID/assets?name=binary-amd64" \ - Network accessline 446
# List workflows curl -s \ - Dangerous shell operationline 447
[Credential-related source omitted] - Network accessline 447
[Credential-related source omitted] - Access to secretsline 447
[Credential-related source omitted] - Network accessline 448
[Credential-related source omitted] - Access to secretsline 448
[Credential-related source omitted] - Network accessline 449
https://api.github.com/repos/$OWNER/$REPO/actions/workflows \ - Network accessline 455
# List recent runs curl -s \ - Dangerous shell operationline 456
[Credential-related source omitted] - Network accessline 456
[Credential-related source omitted] - Access to secretsline 456
[Credential-related source omitted] - Network accessline 457
[Credential-related source omitted] - Access to secretsline 457
[Credential-related source omitted] - Network accessline 458
"https://api.github.com/repos/$OWNER/$REPO/actions/runs?per_page=10" \ - Network accessline 464
# Download failed run logs RUN_ID=<run_id> curl -s -L \ - Network accessline 465
[Credential-related source omitted] - Network accessline 466
[Credential-related source omitted] - Access to secretsline 466
[Credential-related source omitted] - Network accessline 467
[Credential-related source omitted] - Access to secretsline 467
[Credential-related source omitted] - Network accessline 468
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \ - Network accessline 472
# Re-run a failed workflow curl -s -X POST \ - Network accessline 473
[Credential-related source omitted] - Access to secretsline 473
[Credential-related source omitted] - Access to secretsline 474
[Credential-related source omitted] - Network accessline 475
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun - Network accessline 477
# Re-run only failed jobs curl -s -X POST \ - Network accessline 478
[Credential-related source omitted] - Access to secretsline 478
[Credential-related source omitted] - Access to secretsline 479
[Credential-related source omitted] - Network accessline 480
https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/rerun-failed-jobs - Network accessline 482
# Trigger a workflow manually (workflow_dispatch) WORKFLOW_ID=<workflow_id_or_filename> curl -s -X POST \ - Network accessline 483
[Credential-related source omitted] - Network accessline 484
[Credential-related source omitted] - Access to secretsline 484
[Credential-related source omitted] - Network accessline 485
[Credential-related source omitted] - Access to secretsline 485
[Credential-related source omitted] - Network accessline 486
https://api.github.com/repos/$OWNER/$REPO/actions/workflows/$WORKFLOW_ID/dispatches \ - Network accessline 502
# Create a gist curl -s -X POST \ - Network accessline 503
[Credential-related source omitted] - Access to secretsline 503
[Credential-related source omitted] - Network accessline 504
[Credential-related source omitted] - Access to secretsline 504
[Credential-related source omitted] - Network accessline 505
https://api.github.com/gists \ - Network accessline 514
# List your gists curl -s \ - Dangerous shell operationline 515
[Credential-related source omitted] - Network accessline 515
[Credential-related source omitted] - Access to secretsline 515
[Credential-related source omitted] - Network accessline 516
[Credential-related source omitted] - Access to secretsline 516
[Credential-related source omitted] - Network accessline 517
https://api.github.com/gists \ - Network accessline 527
| Action | gh | git + curl | - Network accessline 528
|--------|-----|-----------| | 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 529
| 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 530
| Create repo | `gh repo create name --public` | `curl POST /user/repos` | - Network accessline 531
| Fork | `gh repo fork o/r --clone` | `curl POST /repos/o/r/forks` + `git clone` | - Network accessline 532
| Repo info | `gh repo view o/r` | `curl GET /repos/o/r` | - Network accessline 533
| Edit settings | `gh repo edit --...` | `curl PATCH /repos/o/r` | - Network accessline 534
| Create release | `gh release create v1.0` | `curl POST /repos/o/r/releases` | - Network accessline 535
| List workflows | `gh workflow list` | `curl GET /repos/o/r/actions/workflows` | - Network accessline 536
| Rerun CI | `gh run rerun ID` | `curl POST /repos/o/r/actions/runs/ID/rerun` | - Network accessline 537
| 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 82
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 = "Kheish/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 13
- Integrations - Interop homepage: https://mcporter.dev - Network accessline 14
- Interop homepage: https://mcporter.dev prerequisites: - Network accessline 15
homepage: https://mcporter.dev - Network accessline 36
Use `mcporter` to discover, call, and manage [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers and tools directly from the terminal. - Network accessline 64
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 67
# Connect to any MCP server by URL (no config needed) mcporter list --http-url https://some-mcp-server.com --name my_server - Network accessline 68
mcporter list --http-url https://some-mcp-server.com --name my_server - Network accessline 83
# Ad-hoc HTTP server (no config needed) mcporter call https://api.example.com/mcp.fetch url=https://example.com - Network accessline 84
mcporter call https://api.example.com/mcp.fetch url=https://example.com - Network accessline 86
# Ad-hoc stdio server mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com - Network accessline 87
mcporter call --stdio "bun run ./server.ts" scrape url=https://example.com
skills/mcp/native-mcp/SKILL.md
- Access to secretsline 102
[Credential-related source omitted] - Network accessline 110
mcp_servers: server_name: url: "https://my-server.example.com/mcp" # (required) server URL - Network accessline 111
server_name: url: "https://my-server.example.com/mcp" # (required) server URL headers: # (optional) HTTP headers - Network accessline 112
url: "https://my-server.example.com/mcp" # (required) server URL - Network accessline 196
mcp_servers: remote_api: url: "https://mcp.example.com/mcp" - Network accessline 197
remote_api: url: "https://mcp.example.com/mcp" headers: - Network accessline 198
url: "https://mcp.example.com/mcp" - Access to secretsline 223
[Credential-related source omitted] - Access to secretsline 233
[Credential-related source omitted] - Access to secretsline 309
[Credential-related source omitted] - Network accessline 318
mcp_servers: company_api: url: "https://mcp.mycompany.com/v1/mcp" - Network accessline 319
company_api: url: "https://mcp.mycompany.com/v1/mcp" headers: - Network accessline 320
url: "https://mcp.mycompany.com/v1/mcp" - Access to secretsline 344
[Credential-related source omitted] - Network accessline 346
company_api: url: "https://mcp.internal.company.com/mcp" - Network accessline 347
url: "https://mcp.internal.company.com/mcp"
skills/media/gif-search/SKILL.md
- Network accessline 1
--- 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 - Network accessline 2
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 3
curl and jq. Useful for finding reaction GIFs, creating visual content, and sending - Access to secretsline 9
[Credential-related source omitted] - Network accessline 10
commands: - curl - jq - Network accessline 11
- curl - jq metadata: - Access to secretsline 45
[Credential-related source omitted] - Network accessline 48
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 53
[Credential-related source omitted] - Network accessline 58
# Search and get GIF URLs curl -s "https://tenor.googleapis.com/v2/search?q=thumbs+up&limit=5&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.gif.url' - Network accessline 59
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 61
# Get smaller/preview versions curl -s "https://tenor.googleapis.com/v2/search?q=nice+work&limit=3&key=${TENOR_API_KEY}" | jq -r '.results[].media_formats.tinygif.url' - Network accessline 62
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Network accessline 68
# 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 69
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 70
curl -sL "$URL" -o celebration.gif - Network accessline 76
[Credential-related source omitted] - Access to secretsline 76
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted]
skills/media/heartmula/SKILL.md
- Network accessline 58
cd ~/ # or desired directory git clone https://github.com/HeartMuLa/heartlib.git - Network accessline 59
git clone https://github.com/HeartMuLa/heartlib.git - Network accessline 120
- `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 122
- **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 188
## Links - Repo: https://github.com/HeartMuLa/heartlib - Network accessline 189
- Repo: https://github.com/HeartMuLa/heartlib - Network accessline 190
- Models: https://huggingface.co/HeartMuLa - Network accessline 191
- Paper: https://arxiv.org/abs/2601.10547
skills/media/songsee/SKILL.md
- Network accessline 14
- Music - Analysis homepage: https://github.com/steipete/songsee - Network accessline 15
- Analysis homepage: https://github.com/steipete/songsee prerequisites: - Network accessline 16
homepage: https://github.com/steipete/songsee - Network accessline 41
Requires [Go](https://go.dev/doc/install):
skills/media/youtube-content/SKILL.md
skills/media/youtube-content/scripts/fetch_transcript.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 63
api = YouTubeTranscriptApi() if languages: result = api.fetch(video_id, languages=languages) - Network accessline 64
if languages: result = api.fetch(video_id, languages=languages) else: - Network accessline 65
result = api.fetch(video_id, languages=languages) - Network accessline 67
result = api.fetch(video_id)
skills/mlops/cloud/modal/SKILL.md
- Access to secretsline 261
[Credential-related source omitted] - Network accessline 361
- **Documentation**: https://modal.com/docs - Network accessline 362
- **Examples**: https://github.com/modal-labs/modal-examples - Network accessline 363
- **Pricing**: https://modal.com/pricing - Network accessline 364
- **Discord**: https://discord.gg/modal
skills/mlops/cloud/modal/references/advanced-usage.md
- Network accessline 87
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: - Access to secretsline 431
[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
- Access to secretsline 19
[Credential-related source omitted] - Network accessline 111
).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/lm-evaluation-harness/SKILL.md
- Dangerous shell operationline 214
Avoid for frequent eval (too slow): - Network accessline 514
- GitHub: https://github.com/EleutherAI/lm-evaluation-harness - Network accessline 515
- Docs: https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs - Network accessline 517
- 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] - Network accessline 77
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,\ - Access to secretsline 121
[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 \ - Access to secretsline 269
[Credential-related source omitted] - Access to secretsline 271
[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", - Access to secretsline 302
[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
skills/mlops/evaluation/weights-and-biases/SKILL.md
- Access to secretsline 64
[Credential-related source omitted] - Access to secretsline 571
[Credential-related source omitted] - Network accessline 606
- **Documentation**: https://docs.wandb.ai - Network accessline 607
- **GitHub**: https://github.com/wandb/wandb (10.5k+ stars) - Network accessline 608
- **Examples**: https://github.com/wandb/examples - Network accessline 609
- **Community**: https://wandb.ai/community - Network accessline 610
- **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/huggingface-hub/SKILL.md
- Network accessline 36
## Quick Start * **Installation:** `curl -LsSf https://hf.co/cli/install.sh | bash -s` - Dangerous shell operationline 37
* **Installation:** `curl -LsSf https://hf.co/cli/install.sh | bash -s` - Network accessline 37
* **Installation:** `curl -LsSf https://hf.co/cli/install.sh | bash -s` - Network accessline 52
### Authentication (`hf auth`) * `login` / `logout`: Manage sessions using tokens from [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). - Network accessline 53
* `login` / `logout`: Manage sessions using tokens from [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
skills/mlops/inference/gguf/SKILL.md
- Network accessline 66
# Clone llama.cpp git clone https://github.com/ggml-org/llama.cpp - Network accessline 67
git clone https://github.com/ggml-org/llama.cpp - Network accessline 306
client = OpenAI( base_url="http://localhost:8080/v1", - Network accessline 307
base_url="http://localhost:8080/v1", - Access to secretsline 308
[Credential-related source omitted] - Network accessline 447
- **Repository**: https://github.com/ggml-org/llama.cpp - Network accessline 448
- **Python Bindings**: https://github.com/abetlen/llama-cpp-python - Network accessline 449
- **Pre-quantized Models**: https://huggingface.co/TheBloke - Network accessline 450
- **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 299
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Dangerous shell operationline 411
"calculator": lambda expr: eval(expr), - Network accessline 588
- **Documentation**: https://guidance.readthedocs.io - Network accessline 589
- **GitHub**: https://github.com/guidance-ai/guidance (18k+ stars) - Network accessline 590
- **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] - Access to secretsline 28
[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
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/llama-cpp/SKILL.md
- Network accessline 71
# Or build from source git clone https://github.com/ggerganov/llama.cpp - Network accessline 72
git clone https://github.com/ggerganov/llama.cpp - Network accessline 124
# Client request curl http://localhost:8080/v1/chat/completions \ - Network accessline 125
curl http://localhost:8080/v1/chat/completions \ - Network accessline 273
**Find models**: https://huggingface.co/models?library=gguf - Network accessline 283
- **GitHub**: https://github.com/ggerganov/llama.cpp - Network accessline 284
- **Models**: https://huggingface.co/models?library=gguf - Network accessline 285
- **Discord**: https://discord.gg/llama-cpp
skills/mlops/inference/llama-cpp/references/quantization.md
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 73
git clone https://github.com/elder-plinius/OBLITERATUS.git
skills/mlops/inference/outlines/SKILL.md
- Access to secretsline 254
[Credential-related source omitted] - Network accessline 671
- **Documentation**: https://outlines-dev.github.io/outlines - Network accessline 672
- **GitHub**: https://github.com/outlines-dev/outlines (8k+ stars) - Network accessline 673
- **Discord**: https://discord.gg/R9DSu34mGd - Network accessline 674
- **Blog**: https://blog.dottxt.co
skills/mlops/inference/outlines/references/backends.md
- Access to secretsline 381
[Credential-related source omitted] - Access to secretsline 393
[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
skills/mlops/inference/vllm/SKILL.md
- Network accessline 67
[Credential-related source omitted] - Network accessline 68
[Credential-related source omitted] - Network accessline 69
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 128
# Create test_load.py with sample requests # Run: locust -f test_load.py --host http://localhost:8000 - Network accessline 129
# Run: locust -f test_load.py --host http://localhost:8000 - Network accessline 139
curl http://localhost:9090/metrics | grep vllm - Network accessline 387
- Official docs: https://docs.vllm.ai - Network accessline 388
- GitHub: https://github.com/vllm-project/vllm - Network accessline 390
- 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
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 71
# From GitHub (latest) pip install git+https://github.com/facebookresearch/audiocraft.git - Network accessline 72
pip install git+https://github.com/facebookresearch/audiocraft.git - Network accessline 584
- **GitHub**: https://github.com/facebookresearch/audiocraft - Network accessline 585
- **Paper (MusicGen)**: https://arxiv.org/abs/2306.05284 - Network accessline 586
- **Paper (AudioGen)**: https://arxiv.org/abs/2209.15352 - Network accessline 587
- **HuggingFace**: https://huggingface.co/facebook/musicgen-small - Network accessline 588
- **Demo**: https://huggingface.co/spaces/facebook/MusicGen
skills/mlops/models/audiocraft/references/advanced-usage.md
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 70
pip install git+https://github.com/openai/CLIP.git - Network accessline 277
- **GitHub**: https://github.com/openai/CLIP ⭐ 25,300+ - Network accessline 278
- **Paper**: https://arxiv.org/abs/2103.00020 - Network accessline 279
- **Colab**: https://colab.research.google.com/github/openai/clip/
skills/mlops/models/clip/references/applications.md
skills/mlops/models/segment-anything/SKILL.md
- Network accessline 68
# From GitHub pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 69
pip install git+https://github.com/facebookresearch/segment-anything.git - Network accessline 81
# ViT-H (largest, most accurate) - 2.4GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 82
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth - Network accessline 84
# ViT-L (medium) - 1.2GB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth - Network accessline 85
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth - Network accessline 87
# ViT-B (smallest, fastest) - 375MB wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth - Network accessline 88
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth - Network accessline 516
- **GitHub**: https://github.com/facebookresearch/segment-anything - Network accessline 517
- **Paper**: https://arxiv.org/abs/2304.02643 - Network accessline 518
- **Demo**: https://segment-anything.com - Network accessline 519
- **SAM 2 (Video)**: https://github.com/facebookresearch/segment-anything-2 - Network accessline 520
- **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 542
- **Documentation**: https://huggingface.co/docs/diffusers - Network accessline 543
- **Repository**: https://github.com/huggingface/diffusers - Network accessline 544
- **Model Hub**: https://huggingface.co/models?library=diffusers - Network accessline 545
- **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 76
# Ubuntu: sudo apt install ffmpeg - Network accessline 341
- **GitHub**: https://github.com/openai/whisper ⭐ 72,900+ - Network accessline 342
- **Paper**: https://arxiv.org/abs/2212.04356 - Network accessline 343
- **Model Card**: https://github.com/openai/whisper/blob/main/model-card.md
skills/mlops/models/whisper/references/languages.md
skills/mlops/research/dspy/SKILL.md
- Network accessline 60
# Latest development version pip install git+https://github.com/stanfordnlp/dspy.git - Network accessline 61
pip install git+https://github.com/stanfordnlp/dspy.git - Access to secretsline 321
[Credential-related source omitted] - Access to secretsline 333
[Credential-related source omitted] - Network accessline 342
lm = dspy.OllamaLocal( model="llama3.1", base_url="http://localhost:11434" - Network accessline 343
model="llama3.1", base_url="http://localhost:11434" ) - Network accessline 344
base_url="http://localhost:11434" - Network accessline 607
- **Documentation**: https://dspy.ai - Network accessline 608
- **GitHub**: https://github.com/stanfordnlp/dspy (22k+ stars) - Network accessline 609
- **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/axolotl/SKILL.md
- Network accessline 99
**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 - Network accessline 2121
**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
- Network accessline 9
**URL:** https://docs.axolotl.ai/docs/dataset-formats/tokenized.html - Network accessline 34
**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). - Access to secretsline 2289
[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- - Access to secretsline 2964
[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/grpo-rl-training/README.md
- Network accessline 80
- [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 565
**Official Documentation:** - TRL GRPO Trainer: https://huggingface.co/docs/trl/grpo_trainer - Network accessline 566
- TRL GRPO Trainer: https://huggingface.co/docs/trl/grpo_trainer - Network accessline 567
- DeepSeek R1 Paper: https://arxiv.org/abs/2501.12948 - Network accessline 568
- Unsloth Docs: https://docs.unsloth.ai/ - Network accessline 570
**Example Repositories:** - Open R1 Implementation: https://github.com/huggingface/open-r1 - Network accessline 571
- Open R1 Implementation: https://github.com/huggingface/open-r1 - Network accessline 572
- 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
- Network accessline 457
- **GitHub**: https://github.com/huggingface/peft - Network accessline 458
- **Docs**: https://huggingface.co/docs/peft - Network accessline 459
- **LoRA Paper**: arXiv:2106.09685 - **QLoRA Paper**: arXiv:2305.14314 - **Models**: https://huggingface.co/models?library=peft - Network accessline 461
- **Models**: https://huggingface.co/models?library=peft
skills/mlops/training/peft/references/troubleshooting.md
- Network accessline 18
# 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
- Network accessline 81
**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 87
**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
- Network accessline 9
**URL:** https://pytorch.org/docs/stable/notes/ddp.html - Network accessline 74
**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 - Access to secretsline 1724
[Credential-related source omitted] - Network accessline 1804
**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/trl-fine-tuning/SKILL.md
- Network accessline 480
- Docs: https://huggingface.co/docs/trl/ - Network accessline 481
- GitHub: https://github.com/huggingface/trl - Network accessline 484
- "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 486
- Examples: https://github.com/huggingface/trl/tree/main/examples/scripts
skills/mlops/training/trl-fine-tuning/references/dpo-variants.md
skills/mlops/training/trl-fine-tuning/references/online-rl.md
skills/mlops/training/trl-fine-tuning/references/reward-modeling.md
skills/mlops/training/trl-fine-tuning/references/sft-training.md
skills/mlops/training/unsloth/references/llms-full.md
- Network accessline 5
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 9
<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 11
[Credential-related source omitted] - Network accessline 14
{% column %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 15
{% 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 16
[fine-tuning-llms-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide) - Network accessline 19
{% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 20
[unsloth-notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 25
{% column %} {% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 26
{% content-ref url="all-our-models" %} [all-our-models](https://docs.unsloth.ai/get-started/all-our-models) {% endcontent-ref %} - Network accessline 27
[all-our-models](https://docs.unsloth.ai/get-started/all-our-models) - Network accessline 30
{% 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 31
[tutorials-how-to-fine-tune-and-run-llms](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms) - Network accessline 36
[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 - Network accessline 41
* 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 42
* 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 - Network accessline 47
* 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 48
* 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 49
* **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 50
* [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) works already but a much better version is coming! - Network accessline 51
* 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 53
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 54
[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)**.** - Network accessline 67
For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/install-and-update/windows-installation). - Network accessline 69
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 70
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 75
[**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 81
[**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 95
{% 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) - Network accessline 96
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 99
{% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 100
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 103
[Credential-related source omitted] - Network accessline 108
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/). - Network accessline 112
[Credential-related source omitted] - Network accessline 121
* **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 - Network accessline 122
* 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 - Network accessline 123
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 124
* The official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker Hub. - Network accessline 125
* 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 140
**For context length benchmarks, see** [**here**](https://docs.unsloth.ai/basics/unsloth-benchmarks#context-length-benchmarks)**.** - Network accessline 222
**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 228
**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 247
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 253
{% 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 254
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 262
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 - Network accessline 274
* [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 - Network accessline 276
* [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)  - Network accessline 278
* [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 282
* [**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)  - 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 - Network accessline 297
* [Orpheus-TTS (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb) - Network accessline 298
* [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 && 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
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* 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 && 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) - Network accessline 1299
* Launch the Installer here: [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/) - Network accessline 1344
Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive). - Network accessline 1346
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 1352
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 1361
pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1375
* 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 1389
1. Install `torch` and `triton`. Go to <https://pytorch.org> to install it. For example `pip install torch torchvision torchaudio triton` - Network accessline 1390
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 1391
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 - Network accessline 1399
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). - Network accessline 1400
* Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 1402
* **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 - Network accessline 1403
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 ** - Network accessline 1404
* Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). - Network accessline 1421
* 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) - Network accessline 1422
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 - Network accessline 1423
1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 1429
1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**. - Network accessline 1459
1. Install python though [Python's official site](https://www.python.org/downloads/windows/). - Dangerous shell operationline 1471
sudo apt update && sudo apt upgrade -y - Dangerous shell operationline 1477
sudo apt install python3-pip - Network accessline 1527
Install the latest PyTorch, TorchAO, Xformers from <https://pytorch.org/> - Network accessline 1532
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 - Network accessline 1546
pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 1547
pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 1548
pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth" - Network accessline 1555
And that's it! Try some examples in our [**Unsloth Notebooks**](https://docs.unsloth.ai/get-started/unsloth-notebooks) page! - Network accessline 1559
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 - Network accessline 1564
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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 - Network accessline 1606
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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 - Network accessline 1625
{% 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 1626
Only use Conda if you have it. If not, use [Pip](https://docs.unsloth.ai/get-started/install-and-update/pip-install). - Network accessline 1642
If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below: - Network accessline 1645
mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Network accessline 1646
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh - Dangerous shell operationline 1648
rm -rf ~/miniconda3/miniconda.sh - Network accessline 1658
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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" - Network accessline 1680
# 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") - Network accessline 1681
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" - Network accessline 1688
] # More models at https://huggingface.co/unsloth - Network accessline 1735
# Go to https://docs.unsloth.ai for advanced tips like - Possible obfuscationline 1752
* <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 - Network accessline 1754
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 - Network accessline 1770
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 - Network accessline 1772
{% 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) - Network accessline 1773
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 1783
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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 - Network accessline 1789
* 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 - Network accessline 1801
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 - Network accessline 1803
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 - Network accessline 1807
{% 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) - Network accessline 1808
[what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 1815
* 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 - Network accessline 1816
* 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. - Network accessline 1817
* 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 - Network accessline 1823
{% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 1824
[datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 1827
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 - Network accessline 1831
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 - Network accessline 1835
{% 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 1836
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 1841
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 - Network accessline 1843
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 1844
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 1849
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 1850
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 1859
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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- - Network accessline 1876
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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) - Network accessline 1889
[running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 1892
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 - Network accessline 1894
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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/> - Network accessline 1906
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! - Network accessline 1908
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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 - Network accessline 1923
* **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 - Network accessline 1930
* 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. - Network accessline 1938
* 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- - Network accessline 1939
* 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. - Network accessline 1960
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 - Network accessline 1967
* **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 - Network accessline 1969
* 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 1977
* 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 - Network accessline 1996
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 2019
* 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 2020
* Structured tasks ([classification](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb), summarization, generation tasks). - Network accessline 2049
For [**continued pretraining**](https://docs.unsloth.ai/basics/continued-pretraining), we use raw text format without specific structure: - Network accessline 2057
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 2189
<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 2197
<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 2201
<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 2207
* 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) - Network accessline 2208
* 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 2213
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 2260
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 2262
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 2268
* 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 2269
* Use the [Multiple Datasets](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) notebook to fine-tune on multiple datasets directly. - Network accessline 2281
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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: - Network accessline 2287
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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 2365
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 - Network accessline 2376
| Image - Network accessline 2378
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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 2506
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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 2537
:sloth: *70B LLaMA fits in <48GB VRAM with QLoRA in Unsloth -* [*more details here*](https://unsloth.ai/blog/llama3-3)*.* - Network accessline 2581
[Read our blog post](https://unsloth.ai/blog/gradient) for more details. - Network accessline 2585
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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 2633
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*]( - Network accessline 2634
🦥 *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)*.*\\ - Network accessline 2646
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 - Network accessline 2652
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 - Network accessline 2656
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 - Network accessline 2677
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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 - Network accessline 2704
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. - Network accessline 2709
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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 - Network accessline 2751
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 - Network accessline 2753
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***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 2829
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 - Network accessline 2831
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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> - Network accessline 2835
{% 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 - Network accessline 2836
**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 - Network accessline 2841
[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 - Network accessline 2843
* [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) - Network accessline 2844
* [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing) - Network accessline 2848
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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 - Network accessline 2854
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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. - Network accessline 2957
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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 - Network accessline 2963
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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> - Network accessline 2989
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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 - Network accessline 3063
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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 - Network accessline 3112
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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> - Network accessline 3134
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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 3180
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 3184
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 3197
{% 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) - Network accessline 3198
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) - Network accessline 3206
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 - Network accessline 3229
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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"**< - Network accessline 3259
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 - Network accessline 3268
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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 - Network accessline 3276
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 - Network accessline 3284
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. - Network accessline 3291
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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: - Network accessline 3295
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: - Network accessline 3307
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 - Network accessline 3309
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 - Network accessline 3321
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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 - Network accessline 3374
* **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!** - Network accessline 3377
{% 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 - Network accessline 3378
[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) - Network accessline 3383
| [**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_ - Network accessline 3384
| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- - Network accessline 3385
| [**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 - Network accessline 3386
| [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_\(14B\) - Network accessline 3387
| [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_\ - Network accessline 3389
{% 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 - Network accessline 3390
**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: - Network accessline 3416
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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). - Network accessline 3422
{% 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) - Network accessline 3423
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) - Network accessline 3435
{% 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 - Network accessline 3436
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 - Network accessline 3452
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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: - Network accessline 3539
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 - Network accessline 3559
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 - Network accessline 3560
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]( - Network accessline 3561
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. - Network accessline 3580
{% embed url="<https://www.youtube.com/watch?v=OkEGJ5G3foU>" %} - Network accessline 3584
1. Nathan Lambert's RLHF Book is a must! <https://rlhfbook.com/c/11-policy-gradients.html> - Network accessline 3585
2. Yannic Kilcher's GRPO Youtube video is also a must! <https://www.youtube.com/watch?v=bAWV_yrqx4w> - Network accessline 3586
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> - Network accessline 3587
4. Advanced GRPO notebook via Unsloth. <https://docs.unsloth.ai/basics/reinforcement-learning-guide/tutorial-train-your-own-reasoning-model-with-grpo> - Network accessline 3588
5. GRPO from a base model notebook: <https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb> - Network accessline 3595
DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. - Network accessline 3599
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 3601
| [**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_ - Network accessline 3603
| [**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 3610
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. - Network accessline 3612
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/ - Network accessline 3614
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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] - Network accessline 3630
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 - Network accessline 3632
{% 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) - Network accessline 3633
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) - Network accessline 3636
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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 - Network accessline 3647
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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 - Network accessline 3720
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 - Network accessline 3733
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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** - Network accessline 3742
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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 - Network accessline 3775
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 - Network accessline 3780
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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). - Network accessline 3798
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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 3850
{% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %} - Network accessline 3854
{% embed url="<https://www.youtube.com/watch?t=3289s&v=bbFEYPx9Hpo>" %} - Network accessline 3858
{% embed url="<https://www.youtube.com/watch?v=juOh1afy-IE>" %} - Network accessline 3860
{% embed url="<https://www.youtube.com/watch?v=oF0_eMhzRaQ>" %} - Network accessline 3904
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 - Access to secretsline 4111
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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 - Access to secretsline 4186
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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 - Network accessline 4333
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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 - Network accessline 4349
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 4351
* [**Qwen3 (4B)**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) **-** Advanced GRPO LoRA - Network accessline 4352
* [**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) - Network accessline 4353
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 4354
* [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) - Network accessline 4356
* [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) - Network accessline 4358
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Network accessline 4367
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 4369
<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 - Network accessline 4396
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 - Network accessline 4398
* 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 - Network accessline 4402
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* [GRPO notebooks](https://docs.unsloth.ai/unsloth-notebooks#grpo-reasoning-rl-notebooks) - Network accessline 4459
* [ORPO notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb) - Network accessline 4460
* [DPO Zephyr notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb) - Network accessline 4461
* [KTO notebook](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) - Network accessline 4462
* [SimPO notebook](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing) - Network accessline 4464
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 4471
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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 - Network accessline 4540
> **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 - Network accessline 4563
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly - Access to secretsline 4632
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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 - Network accessline 4659
* 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) - Network accessline 4771
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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) - Network accessline 4784
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): - Dangerous shell operationline 4804
</strong>sudo apt-get update && sudo apt-get install -y \ - Network accessline 4811
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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 - Network accessline 4828
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 4837
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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 - Network accessline 4850
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* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 4936
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 4938
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 4940
* **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 - Network accessline 4946
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 4953
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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 - Network accessline 5006
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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 - Network accessline 5037
# See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 5050
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 - Network accessline 5054
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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!* - Network accessline 5112
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 - Network accessline 5114
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 - Network accessline 5116
* **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\ - Network accessline 5119
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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 - Network accessline 5125
{% 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 - Network accessline 5126
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 - Network accessline 5133
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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). - Network accessline 5137
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 - Network accessline 5145
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 - Network accessline 5194
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 - Network accessline 5196
> *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) - Network accessline 5210
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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). - Network accessline 5227
<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 5229
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 - Network accessline 5251
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 5253
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 5257
| [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 5279
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 5281
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 5283
| [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 5311
!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" \ - Network accessline 5312
"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" \ - Network accessline 5313
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 5314
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 5315
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 - Network accessline 5705
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 - Network accessline 5707
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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 - Network accessline 5719
{% 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 - Network accessline 5720
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 - Network accessline 5729
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>. - Network accessline 5731
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 - Network accessline 5738
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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: - Network accessline 5752
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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 - Network accessline 5816
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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. - Network accessline 5870
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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 - Network accessline 5878
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5879
git clone https://github.com/ggml-org/llama.cpp - Network accessline 5909
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 - Network accessline 5911
[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). - Network accessline 5982
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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 6007
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6008
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6009
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 - Network accessline 6018
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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
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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
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* [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
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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
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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! - 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
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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 - 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
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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 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
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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).  - Network accessline 6675
[Credential-related source omitted] - Network accessline 6679
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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 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.  - 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
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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 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.  - 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
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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 - 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
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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 - Network accessline 7240
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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> - Network accessline 7260
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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 - 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
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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: - 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
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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). - 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 - Dangerous shell operationline 9277
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 - Network accessline 9302
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 - Dangerous shell operationline 9557
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9557
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 - Network accessline 9572
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 - Dangerous shell operationline 9625
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 9625
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)  - 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 - Dangerous shell operationline 10012
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10012
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 - Dangerous shell operationline 10312
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 10312
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 - Network accessline 10332
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!)  * 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 && 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). - Network accessline 868
* Download and install the appropriate version of the **NVIDIA CUDA Toolkit** from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads). - Network accessline 870
* **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 - Network accessline 871
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 ** - Network accessline 872
* Download and install **Microsoft Build Tools for Visual Studio** from the [official website](https://visualstudio.microsoft.com/visual-cpp-build-tools/). - Network accessline 886
* 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) - Network accessline 887
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 - Network accessline 888
1. Download and install **Miniconda** from the [official website](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) - Network accessline 894
1. **Download the** [**unsloth\_windows.ps1**](https://github.com/unslothai/notebooks/blob/main/unsloth_windows.ps1) **PowerShell script by going through this link**. - Network accessline 911
1. Install python though [Python's official site](https://www.python.org/downloads/windows/). - Network accessline 958
pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git" - Network accessline 1014
{% 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! - Network accessline 1015
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! - Network accessline 1022
<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 - Network accessline 1030
According to [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324), these are the recommended settings for inference: - Network accessline 1032
* <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)) - Network accessline 1041
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 1047
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 - Network accessline 1055
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1056
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1057
git clone https://github.com/ggml-org/llama.cpp - Network accessline 1081
> **Try QAT with our free** [**Qwen3 (4B) notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb) - Network accessline 1096
[Credential-related source omitted] - Network accessline 1100
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 - Network accessline 1104
[Credential-related source omitted] - Network accessline 1106
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 - Network accessline 1116
[Credential-related source omitted] - Network accessline 1176
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 1180
[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 1184
**Unsloth** [**Dynamic 2.0**](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) **GGUFs:** - Network accessline 1186
| Model | GGUFs to run: | | ------------------------ | ---------------------- - Network accessline 1187
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Qwen3-**4B-2507** | [Instruct](https://hug - Network accessline 1188
| 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 1189
| 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 1190
| 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 1240
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 1261
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 1263
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 1263
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 1273
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1274
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1275
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 1331
[Credential-related source omitted] - Access to secretsline 1352
[Credential-related source omitted] - Network accessline 1395
curl -X POST http://localhost:8000/v1/load_lora_adapter \ - Network accessline 1405
curl -X POST http://localhost:8000/v1/unload_lora_adapter \ - Network accessline 1436
* 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 - Network accessline 1437
* **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 - Network accessline 1442
* 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. - Network accessline 1448
* 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- - Network accessline 1449
* 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. - Network accessline 1464
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 - Network accessline 1471
* **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 - Network accessline 1473
* 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 - Network accessline 1507
[Credential-related source omitted] - Network accessline 1538
* 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 - Network accessline 1552
* [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. - Network accessline 1555
For more details, check out our ongoing [Pull Request](https://github.com/unslothai/unsloth/issues/2435) discussing multi-GPU support. - Network accessline 1581
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/). - Network accessline 1585
[Credential-related source omitted] - Network accessline 1593
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly - Network accessline 1661
Unsloth supports natively 2x faster inference. For our inference only notebook, click [here](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing). - Network accessline 1679
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: - Network accessline 1700
{% 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) - Network accessline 1701
Please see <https://docs.unsloth.ai/basics/deepseek-r1-0528-how-to-run-locally> for an updated DeepSeek R1-0528 (May 28th 2025 version) - Network accessline 1706
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 - Network accessline 1707
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: - Network accessline 1716
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1717
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 1718
git clone https://github.com/ggerganov/llama.cpp - Network accessline 1770
[Credential-related source omitted] - Network accessline 1776
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[Credential-related source omitted] - Network accessline 1801
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] - Network accessline 1834
[Credential-related source omitted] - Network accessline 1838
<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 - Network accessline 1850
[Credential-related source omitted] - Network accessline 1856
[Credential-related source omitted] - Network accessline 1860
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 - Network accessline 1866
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) - Network accessline 1870
* [Gemma 3 (1B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb) - Network accessline 1871
* [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) - Network accessline 1873
* [Phi-4 (14B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb) - Network accessline 1874
* [Mistral v0.3 (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb) - Network accessline 1875
* [Qwen2.5 (3B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb) - Access to secretsline 1882
[Credential-related source omitted] - Access to secretsline 1899
[Credential-related source omitted] - Network accessline 2027
{% 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 - Network accessline 2028
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 - Network accessline 2033
[Credential-related source omitted] - Network accessline 2045
[Credential-related source omitted] - Network accessline 2049
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). - Network accessline 2051
[Credential-related source omitted] - Network accessline 2060
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! - Network accessline 2117
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 - Network accessline 2122
[Credential-related source omitted] - Network accessline 2130
[Credential-related source omitted] - Network accessline 2137
[Credential-related source omitted] - Network accessline 2139
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 - Network accessline 2159
[Credential-related source omitted] - Network accessline 2169
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 - Network accessline 2183
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 - Network accessline 2188
pip install --no-deps unsloth unsloth-zoo pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 2189
pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git - Network accessline 2190
pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth" - Network accessline 2394
[Credential-related source omitted] - Network accessline 2428
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" "$NOTEBOOK_URL" - Network accessline 2429
NOTEBOOK_URL="https://raw.githubusercontent.com/unslothai/notebooks/refs/heads/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb" - Network accessline 2430
wget -O "gpt_oss_20B_RL_2048_Game.ipynb" "$NOTEBOOK_URL" - Network accessline 2435
[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. - Network accessline 2447
[Credential-related source omitted] - Network accessline 2451
Thanks to Tim from [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) for providing a great fine-tuning tutorial with Unsloth on DGX Spark: - Network accessline 2453
{% embed url="<https://www.youtube.com/watch?t=962s&v=zs-J9sKxvoM>" %} - Network accessline 2471
[Credential-related source omitted] - Network accessline 2478
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 - Network accessline 2480
[Credential-related source omitted] - Network accessline 2504
</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. - Network accessline 2526
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 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 - Network accessline 2541
[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 - Network accessline 2558
[Credential-related source omitted] - Network accessline 2569
[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)  - 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)  - 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 | | ----- - Network accessline 3922
| ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | GGUF: - Network accessline 3923
| 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. - Network accessline 3973
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 3975
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 3975
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 - Network accessline 3987
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 3988
git clone https://github.com/ggerganov/llama.cpp - Network accessline 4001
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) - Access to secretsline 4127
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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 - Network accessline 4211
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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 - Network accessline 4239
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{% 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 - Network accessline 4253
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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 - Network accessline 4321
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 4322
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 - Network accessline 4367
\ 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 - Network accessline 4411
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 - Network accessline 4473
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 4475
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4475
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4499
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. - Network accessline 4519
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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). - Network accessline 4553
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. - Network accessline 4592
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 4594
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 4594
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 && sudo apt-get install -y \ - Network accessline 4654
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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. - Network accessline 4661
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 4668
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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 - Network accessline 4679
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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) - Network accessline 4847
* 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. - Network accessline 4908
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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: - Network accessline 4914
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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 - Network accessline 4994
| Image - Network accessline 4996
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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 - Network accessline 5083
* 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 - Network accessline 5084
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 - Network accessline 5181
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 - Network accessline 5212
| [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 - Network accessline 5217
According to [Qwen](https://huggingface.co/Qwen/QwQ-32B), these are the recommended settings for inference: - Network accessline 5244
Courtesy to [@krist486](https://x.com/krist486/status/1897885598196654180) for bringing llama.cpp sampling directions to my attention. - Network accessline 5248
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 - Network accessline 5260
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 5262
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> - Network accessline 5289
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 5291
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5291
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5310
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 - Network accessline 5312
[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 5322
| <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 5340
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). - Network accessline 5359
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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 5373
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 - Network accessline 5375
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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> - Network accessline 5383
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apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5455
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5456
git clone https://github.com/ggml-org/llama.cpp - Network accessline 5532
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We uploaded dynamic 4-bit quants to: <https://huggingface.co/unsloth/QwQ-32B-unsloth-bnb-4bit> - Network accessline 5536
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! - Network accessline 5538
All our GGUFs are at <https://huggingface.co/unsloth/QwQ-32B-GGUF>! - Network accessline 5548
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! - Network accessline 5607
{% embed url="<https://www.youtube.com/watch?v=SoPE1cUz3Hs>" %} - Network accessline 5611
{% embed url="<https://www.youtube.com/watch?t=3289s&v=bbFEYPx9Hpo>" %} - Network accessline 5615
{% embed url="<https://www.youtube.com/watch?v=juOh1afy-IE>" %} - Network accessline 5617
{% embed url="<https://www.youtube.com/watch?v=oF0_eMhzRaQ>" %} - Network accessline 5650
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\ - Access to secretsline 5696
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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> - Network accessline 5843
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: - Network accessline 5845
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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:/ - Network accessline 5876
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 5908
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 5925
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 5927
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5927
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 5937
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5938
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 5939
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 5965
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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. - Network accessline 6151
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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*]( - Network accessline 6175
🦥 *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)*.*\\ - Network accessline 6179
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 - Network accessline 6181
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 - Network accessline 6185
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 - Network accessline 6206
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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 - Network accessline 6233
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. - Network accessline 6238
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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 - Network accessline 6278
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 - Network accessline 6280
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***Acknowledgements:** A huge thank you to* [*Eyera*](https://huggingface.co/Orenguteng) *for contributing to this guide!* - Network accessline 6374
{% 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) - Network accessline 6375
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) - Network accessline 6383
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 - Network accessline 6404
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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"**< - Network accessline 6434
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 - Network accessline 6443
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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 - Network accessline 6451
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 - Network accessline 6459
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. - Network accessline 6466
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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: - Network accessline 6470
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: - Network accessline 6482
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 - Network accessline 6484
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 - Network accessline 6494
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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 - Network accessline 6545
* **Unsloth now supports** [**RL for Vision/multimodal**](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) **models!** - Network accessline 6548
{% 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 - Network accessline 6549
[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) - Network accessline 6552
| [**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_ - Network accessline 6553
| -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- - Network accessline 6554
| [**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 - Network accessline 6555
| [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_\(14B\) - Network accessline 6556
| [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_\ - Network accessline 6558
{% 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 - Network accessline 6559
**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: - Network accessline 6591
{% 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 6592
[**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 6594
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 6597
> [**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 6599
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 6607
* 20B: [gpt-oss-**20B**](https://huggingface.co/unsloth/gpt-oss-20b-GGUF) - Network accessline 6608
* 120B: [gpt-oss-**120B**](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) - Network accessline 6612
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 6616
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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! - Network accessline 6645
{% 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 6646
[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 6683
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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 - Network accessline 6695
> **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 6703
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 6823
**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 6835
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 - Network accessline 6880
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 6881
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 6882
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 6883
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 6887
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 - Network accessline 6982
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp - Network accessline 6983
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6984
git clone https://github.com/ggerganov/llama.cpp - Network accessline 6993
bash apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6994
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp - Network accessline 6995
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 6996
git clone https://github.com/ggerganov/llama.cpp - Network accessline 7024
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 - Network accessline 7076
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. - Network accessline 7139
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7141
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7141
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7161
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)\ - Network accessline 7168
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 - Network accessline 7186
| Image - Network accessline 7188
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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 7307
[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 - Network accessline 7309
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 - Network accessline 7322
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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** - Network accessline 7331
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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 - Network accessline 7601
!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" \ - Network accessline 7602
"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" \ - Network accessline 7603
"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \ - Network accessline 7604
"unsloth[base] @ git+https://github.com/unslothai/unsloth" \ - Network accessline 7605
git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels - Network accessline 7674
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)\ - Network accessline 7677
**NEW:** DeepSeek-V3.1-Terminus out now: [DeepSeek-V3.1-Terminus-GGUF](https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF)\ - Network accessline 7679
[**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. - Network accessline 7708
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 7709
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: - Network accessline 7774
{% 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 7775
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 7781
{% 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 7783
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 7812
apt-get update apt-get install pciutils -y curl -fsSL https://ollama.com/install.sh | sh - Dangerous shell operationline 7814
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7814
curl -fsSL https://ollama.com/install.sh | sh - Network accessline 7837
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" - Access to secretsline 7935
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year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} - Network accessline 7964
publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} } - Network accessline 7965
howpublished = {\url{https://huggingface.co/unsloth/Qwen3-30B-A3B-GGUF}} - Network accessline 7971
year = {2025}, publisher = {Github}, howpublished = {\url{https://github.com/unslothai/unsloth}} - Network accessline 7972
publisher = {Github}, howpublished = {\url{https://github.com/unslothai/unsloth}} } - Network accessline 7973
howpublished = {\url{https://github.com/unslothai/unsloth}} - Network accessline 8028
* For more detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).  - Network accessline 8029
* 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! - Network accessline 8038
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! - Network accessline 8077
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 - Network accessline 8079
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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: - Network accessline 8098
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sudo apt update && sudo apt install -y wget - Network accessline 8109
sudo apt update && sudo apt install -y wget wget -O Dockerfile "https://raw.githubusercontent.com/unslothai/notebooks/main/Dockerfile_DGX_Spark" - Network accessline 8110
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 - Network accessline 8139
> **Our model upload that enables fine-tuning + more inference support:** [**DeepSeek-OCR**](https://huggingface.co/unsloth/DeepSeek-OCR) - Network accessline 8184
{% 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 - Network accessline 8190
> **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 - Network accessline 8210
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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).  - Network accessline 8226
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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. - Network accessline 8238
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 8243
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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 - Network accessline 8257
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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 - Network accessline 8280
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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
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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. - Network accessline 8331
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). - Network accessline 8364
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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 - Network accessline 8383
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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 - Network accessline 8646
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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 - Network accessline 8682
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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 - Network accessline 8764
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 - Network accessline 8766
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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 - Network accessline 8776
{% 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 - Network accessline 8777
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 - Network accessline 8786
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>. - Network accessline 8788
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 - Network accessline 8795
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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: - Network accessline 8809
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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 - Network accessline 8863
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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. - Network accessline 8902
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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 8925
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8926
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 8927
git clone https://github.com/ggml-org/llama.cpp - Possible obfuscationline 9050
* <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 - Network accessline 9052
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 - Network accessline 9068
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 - Network accessline 9070
{% 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) - Network accessline 9071
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 9081
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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 - Network accessline 9087
* 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 - Network accessline 9099
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 - Network accessline 9101
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 - Network accessline 9105
{% 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) - Network accessline 9106
[what-model-should-i-use](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/what-model-should-i-use) - Network accessline 9111
* 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 - Network accessline 9112
* 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. - Network accessline 9113
* 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 - Network accessline 9119
{% content-ref url="fine-tuning-llms-guide/datasets-guide" %} [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9120
[datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide) - Network accessline 9123
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 - Network accessline 9127
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 - Network accessline 9131
{% 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 9132
[lora-hyperparameters-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide) - Network accessline 9137
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 - Network accessline 9139
{% content-ref url="beginner-start-here/unsloth-requirements" %} [unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 9140
[unsloth-requirements](https://docs.unsloth.ai/get-started/beginner-start-here/unsloth-requirements) - Network accessline 9145
{% content-ref url="install-and-update" %} [install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 9146
[install-and-update](https://docs.unsloth.ai/get-started/install-and-update) - Network accessline 9155
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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- - Network accessline 9170
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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) - Network accessline 9181
[running-and-saving-models](https://docs.unsloth.ai/basics/running-and-saving-models) - Network accessline 9184
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 - Network accessline 9186
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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/> - Network accessline 9196
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! - Network accessline 9198
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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 - Network accessline 9246
# See https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl#qwen-2.5-vl-vision-rl-issues-and-quirks - Network accessline 9292
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide "mention") - Network accessline 9294
***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!* - Network accessline 9311
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 - Network accessline 9320
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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 - Network accessline 9343
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See our guide below for the complete process on how to save to [Ollama](https://github.com/ollama/ollama): - Network accessline 9373
{% 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 - Network accessline 9374
[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) - Network accessline 9379
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 - Network accessline 9381
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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> - Network accessline 9403
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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 - Network accessline 9434
* <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 9435
* 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 9436
* Gemma-3 4B Conversational notebook [**Open in Colab**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) - Network accessline 9437
* 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 9438
* 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 9439
* 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 9440
* **More notebooks in our** [**notebooks docs**](https://docs.unsloth.ai/get-started/unsloth-notebooks) - Network accessline 9463
{% 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 - Network accessline 9464
[**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 9466
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 - Network accessline 9471
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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 - Network accessline 9486
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - Network accessline 9487
| **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 - Network accessline 9488
| **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 - Network accessline 9494
[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 - Network accessline 9496
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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 - Network accessline 9514
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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 - Network accessline 9517
* 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;">**" - Network accessline 9518
* 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, - Network accessline 9524
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. - Network accessline 9525
2. int4 version - presumably [TorchAO int4 style](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md)? - Network accessline 9555
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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) - Network accessline 9598
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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 - Network accessline 9600
* 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. - Network accessline 9601
* [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 - Network accessline 9603
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apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9618
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 9619
git clone https://github.com/ggml-org/llama.cpp - Network accessline 9652
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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. - Network accessline 9664
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 9666
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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 - Network accessline 9676
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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)**. - Network accessline 9692
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 9698
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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 - Network accessline 9716
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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> - Network accessline 9728
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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 - Network accessline 9774
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
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 9800
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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: - Network accessline 9820
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: - Access to secretsline 9868
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* Use `USER_PASSWORD` for sudo operations inside container - Network accessline 9949
{% 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 - Network accessline 9951
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. - Network accessline 9958
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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) - Network accessline 9986
DeepSeek developed [GRPO](https://unsloth.ai/blog/grpo) (Group Relative Policy Optimization) to train their R1 reasoning models. - Network accessline 9988
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_ - Network accessline 9992
| [**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. - Network accessline 9999
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/ - Network accessline 10001
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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] - Network accessline 10013
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 - Network accessline 10015
{% 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) - Network accessline 10016
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) - Network accessline 10019
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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 - Network accessline 10026
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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) - Network accessline 10048
**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 10051
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 10055
[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 10061
| Dynamic 2.0 GGUF (to run) - Network accessline 10063
| <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 10098
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 10136
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 10183
## 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 - Network accessline 10195
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 - Network accessline 10197
* 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 - Network accessline 10201
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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 - Network accessline 10272
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 - Network accessline 10278
| [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) - Network accessline 10312
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 - Network accessline 10314
* [`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 - Network accessline 10315
* [`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 - Network accessline 10363
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. - Network accessline 10399
{% 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 10401
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 10425
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10426
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10427
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 \ - Network accessline 10481
See <https://docs.sglang.ai/get_started/install.html> for more details - Network accessline 10546
<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 10554
| MoE Bits | Type | Disk Size | HF Link | | -------- | --------- | --------- | ---------------------------------------------------------------- - Network accessline 10555
| -------- | --------- | --------- | --------------------------------------------------------------------------------------------------- | | 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E- - Network accessline 10556
| 1.78bit | IQ1\_S | 122GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_S) | - Network accessline 10557
| 1.93bit | IQ1\_M | 128GB | [Link](https://huggingface.co/unsloth/Llama-4-Maverick-17B-128E-Instruct-GGUF/tree/main/UD-IQ1_M) | - Network accessline 10558
| 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 10559
| 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 10560
| 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 10561
| 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 10574
* A BOS token of `<|begin_of_text|>` is auto added during tokenization (do NOT add it manually!)  * According to <https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/>, there is an <mark style="background-color:yellow - Network accessline 10575
* 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 10579
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 10581
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 - Network accessline 10607
apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10608
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y - Network accessline 10609
git clone https://github.com/ggml-org/llama.cpp - Access to secretsline 10634
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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 - Network accessline 10767
* DeepSeek-OCR: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb) - Network accessline 10768
* DeepSeek-OCR: [Fine-tuning + Evaluation notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\)-Eval.ipynb) (A100) - Network accessline 10800
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 - Network accessline 10802
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 - Network accessline 10804
* **Free notebook:** [**gpt-oss-20b GRPO Colab notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb)\ - Network accessline 10807
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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 - Network accessline 10813
{% 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 - Network accessline 10814
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 - Network accessline 10821
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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). - Network accessline 10825
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 - Network accessline 10833
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 - Network accessline 10855
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 - Network accessline 10857
> *Quote from* [*https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665*](https://github.com/meta-pytorch/attention-gym/issues/15#issuecomment-2284148665) - Network accessline 10871
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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). - Network accessline 10888
<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 10890
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 - Network accessline 10908
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 10910
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 10914
| [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 10974
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. - Network accessline 10976
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). - Network accessline 10978
> **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) - Network accessline 11024
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 - Network accessline 11026
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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> - Network accessline 11030
{% 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 - Network accessline 11031
**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 - Network accessline 11036
[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 - Network accessline 11038
* [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) - Network accessline 11039
* [CSV/Excel Ollama Guide](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS?usp=sharing) - Network accessline 11043
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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 - Network accessline 11049
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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. - Network accessline 11121
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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 - Network accessline 11127
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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> - Network accessline 11153
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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 - Network accessline 11225
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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 - Network accessline 11264
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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> - Network accessline 11286
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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 - Network accessline 11481
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
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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) - 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) - Network accessline 11533
{% 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) - Network accessline 11593
[faq-+-is-fine-tuning-right-for-me](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me) - Network accessline 11596
{% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 11597
[reinforcement-learning-rl-guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) - Network accessline 11600
[Credential-related source omitted] - Network accessline 11706
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 && sudo apt-get install -y \ - Network accessline 11739
[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
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Go to [http://localhost:8888](http://localhost:8888/) and open Unsloth. - Network accessline 11753
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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 - Network accessline 11764
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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). - 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/productivity/google-workspace/SKILL.md
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[Credential-related source omitted] - Network accessline 23
- OAuth - gws homepage: https://github.com/googleworkspace/cli - Network accessline 24
- gws homepage: https://github.com/googleworkspace/cli related_skills: - Network accessline 25
homepage: https://github.com/googleworkspace/cli - Network accessline 123
> 1. Go to https://console.cloud.google.com/apis/credentials - Access to secretsline 130
[Credential-related source omitted]
skills/productivity/google-workspace/scripts/google_api.py
skills/productivity/google-workspace/scripts/gws_bridge.py
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#!/usr/bin/env python3 - Access to secretsline 15
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req = urllib.request.Request(token_data["token_uri"], data=params) - Network accessline 36
try: with urllib.request.urlopen(req) as resp: result = json.loads(resp.read()) - Network accessline 37
with urllib.request.urlopen(req) as resp: - Access to secretsline 45
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skills/productivity/google-workspace/scripts/setup.py
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#!/usr/bin/env python3 - Access to secretsline 17
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SCOPES = [ "https://www.googleapis.com/auth/gmail.readonly", - Network accessline 47
"https://www.googleapis.com/auth/gmail.readonly", - Network accessline 48
"https://www.googleapis.com/auth/gmail.send", - Network accessline 49
"https://www.googleapis.com/auth/gmail.modify", - Network accessline 50
"https://www.googleapis.com/auth/calendar", - Network accessline 51
"https://www.googleapis.com/auth/drive.readonly", - Network accessline 52
"https://www.googleapis.com/auth/contacts.readonly", - Network accessline 53
"https://www.googleapis.com/auth/spreadsheets", - Network accessline 54
"https://www.googleapis.com/auth/documents.readonly", - Network accessline 62
REDIRECT_URI = "http://localhost:1" - Access to secretsline 172
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Network accessline 185
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 186
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 187
print("Download the correct file from: https://console.cloud.google.com/apis/credentials") - Access to secretsline 310
[Credential-related source omitted] - Network accessline 356
import urllib.request urllib.request.urlopen( - Network accessline 357
urllib.request.urlopen( - Network accessline 358
urllib.request.Request( - Network accessline 359
[Credential-related source omitted] - Access to secretsline 377
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[Credential-related source omitted]
skills/productivity/linear/SKILL.md
- Network accessline 2
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 3
update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies. version: 1.0.0 - Network accessline 4
via curl — no dependencies. - Access to secretsline 9
[Credential-related source omitted] - Network accessline 10
commands: - curl metadata: - Network accessline 11
- curl metadata: catalog: - Access to secretsline 43
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Base curl pattern: - Dangerous shell operationline 54
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# First page curl -s -X POST https://api.linear.app/graphql \ - Dangerous shell operationline 275
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# Next page — use endCursor from previous response curl -s -X POST https://api.linear.app/graphql \ - Dangerous shell operationline 281
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skills/productivity/nano-pdf/SKILL.md
skills/productivity/notion/SKILL.md
- Network accessline 13
- Database - API homepage: https://developers.notion.com - Network accessline 14
- API homepage: https://developers.notion.com prerequisites: - Network accessline 15
homepage: https://developers.notion.com - Access to secretsline 18
[Credential-related source omitted] - Network accessline 36
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 40
1. Create an integration at https://notion.so/my-integrations - Access to secretsline 44
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- **Checkbox:** `{"checkbox": true}` - **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}` - Network accessline 170
- **Number:** `{"number": 42}` - **URL:** `{"url": "https://..."}` - **Email:** `{"email": "user@example.com"}` - Network accessline 171
- **URL:** `{"url": "https://..."}` - Network accessline 187
- 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 Kheish) - Network accessline 188
- 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 Kheish) - Pipe output through `jq` for readable JSON: `... | jq '.results[0].properties'` - Network accessline 189
- Add `-s` flag to curl to suppress progress bars (cleaner output for Kheish)
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 44
web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 45
web_extract(urls=["https://example.com/report.pdf"]) - Network accessline 135
# Abstract only (fast) web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 136
web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 138
# Full paper web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 139
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
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
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
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
<schema . . .> . . . <import namespace="http://www.w3.org/XML/1998/namespace" - Network accessline 56
. . . <import namespace="http://www.w3.org/XML/1998/namespace" schemaLocation="http://www.w3.org/2001/03/xml.xsd"/> - Network accessline 57
<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/red-teaming/godmode/SKILL.md
- Network accessline 40
Bypass safety filters on API-served LLMs using techniques from [G0DM0D3](https://github.com/elder-plinius/G0DM0D3) and [L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S). Three attack modes plus Kheish-native configuration for persistent - Dangerous shell operationline 87
exec(open(os.path.expanduser( - Dangerous shell operationline 104
**Important:** Always use `load_godmode.py` instead of loading individual scripts directly. The individual scripts have `argparse` CLI entry points and `__name__` guards that break when loaded via `exec()` in execute_code. The loader handle - Dangerous shell operationline 220
exec(open(os.path.expanduser(".agents/skills/red-teaming/godmode/scripts/parseltongue.py")).read()) - Dangerous shell operationline 257
exec(open(os.path.expanduser(".agents/skills/red-teaming/godmode/scripts/godmode_race.py")).read()) - Access to secretsline 262
[Credential-related source omitted] - Network accessline 312
[Credential-related source omitted] - Network accessline 313
[Credential-related source omitted] - Access to secretsline 313
[Credential-related source omitted] - Network accessline 314
base_url="https://openrouter.ai/api/v1" - Network accessline 374
- **G0DM0D3:** [elder-plinius/G0DM0D3](https://github.com/elder-plinius/G0DM0D3) (AGPL-3.0) - Network accessline 375
- **L1B3RT4S:** [elder-plinius/L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S) (AGPL-3.0) - Network accessline 376
- **Pliny the Prompter:** [@elder_plinius](https://x.com/elder_plinius) - Dangerous shell operationline 425
9. **Always use `load_godmode.py` in execute_code** — The individual scripts (`parseltongue.py`, `godmode_race.py`, `auto_jailbreak.py`) have argparse CLI entry points with `if __name__ == '__main__'` blocks. When loaded via `exec()` in exe - Access to secretsline 428
[Credential-related source omitted]
skills/red-teaming/godmode/references/jailbreak-templates.md
- Network accessline 3
Source: [G0DM0D3](https://github.com/elder-plinius/G0DM0D3) + [L1B3RT4S](https://github.com/elder-plinius/L1B3RT4S) - Dangerous shell operationline 117
exec(open(os.path.expanduser("~/.kheish/skills/red-teaming/godmode/scripts/godmode_race.py")).read()) - Network accessline 126
- The system prompt in GODMODE CLASSIC keeps `{QUERY}` as literal text (the variable name is part of the jailbreak frame) - Only the user message gets the actual query injected - Templates source: [L1B3RT4S repo](https://github.com/elder-pl - Network accessline 128
- Templates source: [L1B3RT4S repo](https://github.com/elder-plinius/L1B3RT4S) by Pliny the Prompter
skills/red-teaming/godmode/references/refusal-detection.md
- Dangerous shell operationline 132
exec(open(os.path.expanduser("~/.kheish/skills/red-teaming/godmode/scripts/godmode_race.py")).read())
skills/red-teaming/godmode/scripts/auto_jailbreak.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 9
exec(open(os.path.expanduser( - Dangerous shell operationline 33
# Resolve skill directory — works both as direct script and via exec() - Dangerous shell operationline 37
# __file__ not defined when loaded via exec() — search standard paths - Dangerous shell operationline 52
exec(compile(open(_parseltongue_path).read(), str(_parseltongue_path), 'exec'), _caller_globals) - Dangerous shell operationline 54
exec(compile(open(_race_path).read(), str(_race_path), 'exec'), _caller_globals) - Network accessline 329
model_cfg = cfg.get("model", {}) if isinstance(model_cfg, str): return model_cfg, "https://openrouter.ai/api/v1" - Network accessline 330
if isinstance(model_cfg, str): return model_cfg, "https://openrouter.ai/api/v1" model_name = model_cfg.get("name", "") - Network accessline 331
return model_cfg, "https://openrouter.ai/api/v1" - Network accessline 332
model_name = model_cfg.get("name", "") base_url = model_cfg.get("base_url", "https://openrouter.ai/api/v1") return model_name, base_url - Network accessline 333
base_url = model_cfg.get("base_url", "https://openrouter.ai/api/v1") - Access to secretsline 342
[Credential-related source omitted] - Access to secretsline 344
[Credential-related source omitted] - Access to secretsline 346
[Credential-related source omitted] - Access to secretsline 348
[Credential-related source omitted] - Access to secretsline 420
[Credential-related source omitted] - Access to secretsline 432
[Credential-related source omitted] - Network accessline 450
return {"success": False, "error": "No model specified and couldn't read config.yaml"} if not base_url: base_url = "https://openrouter.ai/api/v1" - Network accessline 451
[Credential-related source omitted] - Network accessline 452
base_url = "https://openrouter.ai/api/v1" - Access to secretsline 453
[Credential-related source omitted] - Access to secretsline 454
[Credential-related source omitted] - Access to secretsline 455
[Credential-related source omitted] - Access to secretsline 469
[Credential-related source omitted]
skills/red-teaming/godmode/scripts/godmode_race.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 10
exec(open(os.path.expanduser("~/.kheish/skills/red-teaming/godmode/scripts/godmode_race.py")).read()) - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Access to secretsline 323
[Credential-related source omitted] - Access to secretsline 324
[Credential-related source omitted] - Access to secretsline 325
[Credential-related source omitted] - Network accessline 327
[Credential-related source omitted] - Access to secretsline 327
[Credential-related source omitted] - Access to secretsline 400
[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] - Network accessline 452
[Credential-related source omitted] - Access to secretsline 452
[Credential-related source omitted]
skills/red-teaming/godmode/scripts/load_godmode.py
skills/red-teaming/godmode/scripts/parseltongue.py
skills/research/arxiv/SKILL.md
- Network accessline 39
| Action | Command | |--------|---------| | Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | - Network accessline 40
|--------|---------| | 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 41
| Search papers | `curl "https://export.arxiv.org/api/query?search_query=all:QUERY&max_results=5"` | - Network accessline 42
| Get specific paper | `curl "https://export.arxiv.org/api/query?id_list=2402.03300"` | - Network accessline 43
| Read abstract (web) | `web_extract(urls=["https://arxiv.org/abs/2402.03300"])` | - Network accessline 44
| Read full paper (PDF) | `web_extract(urls=["https://arxiv.org/pdf/2402.03300"])` | - Network accessline 53
curl -s "https://export.arxiv.org/api/query?search_query=all:GRPO+reinforcement+learning&max_results=5" - Dangerous shell operationline 59
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 59
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 60
import sys, xml.etree.ElementTree as ET ns = {'a': 'http://www.w3.org/2005/Atom'} root = ET.parse(sys.stdin).getroot() - Network accessline 61
ns = {'a': 'http://www.w3.org/2005/Atom'} - Network accessline 72
print(f' Published: {published} | Categories: {cats}') print(f' Abstract: {summary}...') print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') - Network accessline 73
print(f' Abstract: {summary}...') print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') print() - Network accessline 74
print(f' PDF: https://arxiv.org/pdf/{arxiv_id}') - Network accessline 119
# 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 120
curl -s "https://export.arxiv.org/api/query?search_query=cat:cs.AI&sortBy=submittedDate&sortOrder=descending&max_results=10" - Network accessline 126
# By arXiv ID curl -s "https://export.arxiv.org/api/query?id_list=2402.03300" - Network accessline 127
curl -s "https://export.arxiv.org/api/query?id_list=2402.03300" - Network accessline 129
# Multiple papers curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001" - Network accessline 130
curl -s "https://export.arxiv.org/api/query?id_list=2402.03300,2401.12345,2403.00001" - Dangerous shell operationline 139
curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python3 -c " - Network accessline 139
curl -s "https://export.arxiv.org/api/query?id_list=1706.03762" | python3 -c " - Network accessline 140
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 141
ns = {'a': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} - Network accessline 157
print(f' archivePrefix = {{arXiv}},') print(f' primaryClass = {{{primary}}},') print(f' url = {{https://arxiv.org/abs/{raw_id}}}') - Network accessline 158
print(f' primaryClass = {{{primary}}},') print(f' url = {{https://arxiv.org/abs/{raw_id}}}') print('}') - Network accessline 159
print(f' url = {{https://arxiv.org/abs/{raw_id}}}') - Network accessline 170
# Abstract page (fast, metadata + abstract) web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 171
web_extract(urls=["https://arxiv.org/abs/2402.03300"]) - Network accessline 173
# Full paper (PDF → markdown via Firecrawl) web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 174
web_extract(urls=["https://arxiv.org/pdf/2402.03300"]) - Network accessline 192
Full list: https://arxiv.org/category_taxonomy - Network accessline 218
# 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 219
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 219
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 221
# 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 222
curl -s "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example?fields=title,citationCount" - Dangerous shell operationline 228
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 228
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 234
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 234
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 240
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 240
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 246
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 246
curl -s -X POST "https://api.semanticscholar.org/recommendations/v1/papers/" \ - Dangerous shell operationline 254
curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python3 -m json.tool - Network accessline 254
curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=Yann+LeCun&fields=name,hIndex,citationCount,paperCount" | python3 -m json.tool - Network accessline 265
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 266
2. **Assess impact**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID?fields=citationCount,influentialCitationCount"` - Network accessline 267
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 268
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 269
5. **Find related work**: `curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:ID/references?fields=title,citationCount&limit=20"` - Network accessline 271
7. **Track authors**: `curl -s "https://api.semanticscholar.org/graph/v1/author/search?query=NAME"` - Network accessline 283
- 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 284
- 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 285
- PDF: `https://arxiv.org/pdf/{id}` — Abstract: `https://arxiv.org/abs/{id}` - Network accessline 286
- HTML (when available): `https://arxiv.org/html/{id}` - Network accessline 292
- `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 294
- 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': 'Kheish/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 12
- Feed-Reader - Monitoring homepage: https://github.com/JulienTant/blogwatcher-cli - Network accessline 13
- Monitoring homepage: https://github.com/JulienTant/blogwatcher-cli prerequisites: - Network accessline 14
homepage: https://github.com/JulienTant/blogwatcher-cli - Network accessline 41
- **Go:** `go install github.com/JulienTant/blogwatcher-cli/cmd/blogwatcher-cli@latest` - **Docker:** `docker run --rm -v blogwatcher-cli:/data ghcr.io/julientant/blogwatcher-cli` - **Binary (Linux amd64):** `curl -sL https://github.com/Jul - Network accessline 42
- **Docker:** `docker run --rm -v blogwatcher-cli:/data ghcr.io/julientant/blogwatcher-cli` - **Binary (Linux amd64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_amd64.tar.gz | ta - Network accessline 43
- **Binary (Linux amd64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_amd64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 44
- **Binary (Linux arm64):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_linux_arm64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 45
- **Binary (macOS Apple Silicon):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_darwin_arm64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 46
- **Binary (macOS Intel):** `curl -sL https://github.com/JulienTant/blogwatcher-cli/releases/latest/download/blogwatcher-cli_darwin_amd64.tar.gz | tar xz -C /usr/local/bin blogwatcher-cli` - Network accessline 48
All releases: https://github.com/JulienTant/blogwatcher-cli/releases - Network accessline 76
- Add a blog: `blogwatcher-cli add "My Blog" https://example.com` - Network accessline 77
- Add with explicit feed: `blogwatcher-cli add "My Blog" https://example.com --feed-url https://example.com/feed.xml` - Network accessline 78
- Add with HTML scraping: `blogwatcher-cli add "My Blog" https://example.com --scrape-selector "article h2 a"` - Instruction override signalline 103
| `BLOGWATCHER_WORKERS` | Number of concurrent scan workers (default: 8) | | `BLOGWATCHER_SILENT` | Only output "scan done" when scanning | | `BLOGWATCHER_YES` | Skip confirmation prompts | - Instruction override signalline 104
| `BLOGWATCHER_SILENT` | Only output "scan done" when scanning | | `BLOGWATCHER_YES` | Skip confirmation prompts | | `BLOGWATCHER_CATEGORY` | Default filter for articles by category | - Instruction override signalline 105
| `BLOGWATCHER_YES` | Skip confirmation prompts | - Network accessline 114
xkcd URL: https://xkcd.com - Network accessline 115
URL: https://xkcd.com - Network accessline 116
Feed: https://xkcd.com/atom.xml - Network accessline 134
[1] [new] Barrel - Part 13 Blog: xkcd URL: https://xkcd.com/3095/ - Network accessline 135
Blog: xkcd URL: https://xkcd.com/3095/ Published: 2026-04-02 - Network accessline 136
URL: https://xkcd.com/3095/ - Network accessline 140
[2] [new] Volcano Fact Blog: xkcd URL: https://xkcd.com/3094/ - Network accessline 141
Blog: xkcd URL: https://xkcd.com/3094/ Published: 2026-04-01 - Network accessline 142
URL: https://xkcd.com/3094/
skills/research/llm-wiki/SKILL.md
- Network accessline 42
Build and maintain a persistent, compounding knowledge base as interlinked markdown files. Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). - Network accessline 43
Based on [Andrej Karpathy's LLM Wiki pattern](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f). - Dangerous shell operationline 454
sudo loginctl enable-linger $USER
skills/research/polymarket/SKILL.md
- Network accessline 30
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": "Kheish/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/research/research-paper-writing/SKILL.md
- Network accessline 300
# Via web_extract (for specific papers): web_extract("https://arxiv.org/abs/2303.17651") - Network accessline 301
web_extract("https://arxiv.org/abs/2303.17651") - Network accessline 316
claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp" - Network accessline 370
def doi_to_bibtex(doi: str) -> str: response = requests.get( - Network accessline 371
response = requests.get( - Network accessline 372
f"https://doi.org/{doi}", - Dangerous shell operationline 1514
# Linux: sudo apt install latexdiff - Network accessline 1742
- [ ] Self-citations written in third person: "Smith et al. [1] showed..." not "We previously showed [1]..." - [ ] No GitHub/GitLab URLs pointing to your personal repos - [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code - Network accessline 1743
- [ ] No GitHub/GitLab URLs pointing to your personal repos - [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code links - [ ] No institutional logos or identifiers in figures - Network accessline 1744
- [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code links - Dangerous shell operationline 1819
rm -f *.aux *.bbl *.blg *.log *.out *.pdf - Network accessline 1994
# Use Anonymous GitHub for double-blind review # https://anonymous.4open.science/ - Network accessline 1995
# https://anonymous.4open.science/ - Network accessline 2167
| **`read_file`** / **`write_file`** / **`patch`** | Paper editing, experiment scripts, result files. Use `patch` for targeted edits to large .tex files. | | **`web_search`** | Literature discovery: `web_search("transformer attention mechan - Network accessline 2168
| **`web_search`** | Literature discovery: `web_search("transformer attention mechanism 2024")` | | **`web_extract`** | Fetch paper content, verify citations: `web_extract("https://arxiv.org/abs/2303.17651")` | | **`delegate_task`** | **Par - Network accessline 2169
| **`web_extract`** | Fetch paper content, verify citations: `web_extract("https://arxiv.org/abs/2303.17651")` | - Network accessline 2213
doi = paper.externalIds.get('DOI', 'N/A') if doi != 'N/A': bibtex = requests.get(f"https://doi.org/{doi}", - Network accessline 2214
if doi != 'N/A': bibtex = requests.get(f"https://doi.org/{doi}", headers={"Accept": "application/x-bibtex"}).text - Network accessline 2215
bibtex = requests.get(f"https://doi.org/{doi}", - Network accessline 2392
**Writing Philosophy:** - [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Network accessline 2393
- [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Network accessline 2394
- [Sebastian Farquhar: How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) - Network accessline 2395
- [Gopen & Swan: Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) - Network accessline 2396
- [Lipton: Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) - Network accessline 2397
- [Perez: Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) - Network accessline 2399
**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 2401
**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/research-paper-writing/references/autoreason-methodology.md
- Network accessline 5
**Source**: [NousResearch/autoreason](https://github.com/NousResearch/autoreason) — "Autoreason: When Iterative LLM Refinement Works and Why It Fails"
skills/research/research-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/research-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 | - Network accessline 80
| Template | [GitHub](https://github.com/COLM-org/Template) | LaTeX templates | - Network accessline 88
| 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 - Network accessline 90
| **Semantic Scholar** | [Docs](https://api.semanticscholar.org/api-docs/) | ML/AI papers, citation graphs | - Network accessline 91
| **CrossRef** | [Docs](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | DOI lookup, BibTeX retrieval | - Network accessline 92
| **arXiv** | [Docs](https://info.arxiv.org/help/api/basics.html) | Preprints, PDF access | - Network accessline 93
| **OpenAlex** | [Docs](https://docs.openalex.org/) | Open alternative, bulk access | - Network accessline 105
| Tool | URL | Purpose | |------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | - Network accessline 106
|------|-----|---------| | Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | | ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking | - Network accessline 107
| Citely | [citely.ai](https://citely.ai/citation-checker) | Batch verification | - Network accessline 108
| ReciteWorks | [reciteworks.com](https://reciteworks.com/) | In-text citation checking | - Network accessline 116
| 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 | - Network accessline 118
| PlotNeuralNet | [GitHub](https://github.com/HarisIqbal88/PlotNeuralNet) | TikZ neural network diagrams | - Network accessline 119
| SciencePlots | [GitHub](https://github.com/garrettj403/SciencePlots) | Publication-ready matplotlib | - Network accessline 120
| 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 | - Network accessline 127
| BibLaTeX Guide | [CTAN](https://ctan.org/pkg/biblatex) | Modern citation management | - Network accessline 133
| 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 | - Network accessline 171
| Source | URL | Key Contribution | |--------|-----|------------------| | **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | - Network accessline 172
|--------|-----|------------------| | **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | | **Model Cards for Model Reporting** | Mitchell et al., 2019 - Network accessline 173
| **Datasheets for Datasets** | Gebru et al., 2021 ([arXiv](https://arxiv.org/abs/1803.09010)) | Structured dataset documentation framework | - Network accessline 174
| **Model Cards for Model Reporting** | Mitchell et al., 2019 ([arXiv](https://arxiv.org/abs/1810.03993)) | Structured model documentation framework | - Network accessline 175
| **Crowdsourcing and Human Computation** | [Survey](https://arxiv.org/abs/2202.06516) | Best practices for crowdsourced annotation | - Network accessline 176
| **Krippendorff's Alpha** | [Wikipedia](https://en.wikipedia.org/wiki/Krippendorff%27s_alpha) | Inter-annotator agreement metric reference | - Network accessline 177
| **Prolific** | [prolific.co](https://www.prolific.co/) | Recommended crowdsourcing platform for research | - Network accessline 181
| Source | URL | Key Contribution | |--------|-----|------------------| | **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | - Network accessline 182
|--------|-----|------------------| | **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | | **NeurIPS Broader Impact Guide** | [NeurIPS](https://neurips.cc/public/guides/PaperCheckl - Network accessline 183
| **ML CO2 Impact** | [mlco2.github.io](https://mlco2.github.io/impact/) | Compute carbon footprint calculator | - Network accessline 184
| **NeurIPS Broader Impact Guide** | [NeurIPS](https://neurips.cc/public/guides/PaperChecklist) | Official guidance on impact statements | - Network accessline 185
| **ACL Ethics Policy** | [ACL](https://www.aclweb.org/portal/content/acl-code-ethics) | Ethics requirements for NLP research |
skills/research/research-paper-writing/templates/README.md
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**Setup:** 1. Install [TeX Live](https://www.tug.org/texlive/) (full distribution recommended) - Network accessline 12
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/) - Dangerous shell operationline 14
- Ubuntu: `sudo apt install texlive-full` - Network accessline 15
- Windows: Download from [tug.org/texlive](https://www.tug.org/texlive/) - Network accessline 62
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) | - Network accessline 110
| ICML | `icml2026/` | 2026 | [Official ICML](https://icml.cc/Conferences/2026/AuthorInstructions) | - Network accessline 111
| ICLR | `iclr2026/` | 2026 | [Official GitHub](https://github.com/ICLR/Master-Template) | - Network accessline 112
| 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) | - Network accessline 114
| AAAI | `aaai2026/` | 2026 | [AAAI Author Kit](https://aaai.org/authorkit26/) | - Network accessline 115
| COLM | `colm2025/` | 2025 | [Official COLM](https://github.com/COLM-org/Template) | - Network accessline 246
- 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 - Network accessline 250
- AAAI: https://aaai.org/ - Network accessline 251
- COLM: https://colmweb.org/
skills/research/research-paper-writing/templates/aaai2026/README.md
skills/research/research-paper-writing/templates/aaai2026/aaai2026-unified-supp.tex
skills/research/research-paper-writing/templates/aaai2026/aaai2026-unified-template.tex
- Dangerous shell operationline 121
Written by AAAI Press Staff\textsuperscript{\rm 1}\thanks{With help from the AAAI Publications Committee.}\\ - Dangerous shell operationline 132
\textsuperscript{\rm 1}Association for the Advancement of Artificial Intelligence\\ - Dangerous shell operationline 137
% Sunil Issar\textsuperscript{\rm 2}, - Dangerous shell operationline 138
% J. Scott Penberthy\textsuperscript{\rm 3}, - Dangerous shell operationline 139
% George Ferguson\textsuperscript{\rm 4}, - Dangerous shell operationline 140
% Hans Guesgen\textsuperscript{\rm 5} - Dangerous shell operationline 169
First Author Name\textsuperscript{\rm 1}, - Dangerous shell operationline 170
Second Author Name\textsuperscript{\rm 2}, - Dangerous shell operationline 171
Third Author Name\textsuperscript{\rm 1} - Dangerous shell operationline 175
\textsuperscript{\rm 1}Affiliation 1\\ - Dangerous shell operationline 176
\textsuperscript{\rm 2}Affiliation 2\\ - Network accessline 198
% NOTE: For anonymous submissions, do not include links that could reveal your identity % \begin{links} % \link{Code}{https://aaai.org/example/code} - Network accessline 199
% \begin{links} % \link{Code}{https://aaai.org/example/code} % \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 200
% \link{Code}{https://aaai.org/example/code} - Network accessline 201
% \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 202
% \link{Extended version}{https://aaai.org/example/extended-version} - Network accessline 206
% 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} - Network accessline 207
\begin{links} \link{Code}{https://aaai.org/example/code} \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 208
\link{Code}{https://aaai.org/example/code} - Network accessline 209
\link{Datasets}{https://aaai.org/example/datasets} - Network accessline 210
\link{Extended version}{https://aaai.org/example/extended-version} - Network accessline 405
\begin{scriptsize}\begin{verbatim} \begin{links} \link{Code}{https://aaai.org/example/guidelines} - Network accessline 406
\begin{links} \link{Code}{https://aaai.org/example/guidelines} \link{Datasets}{https://aaai.org/example/datasets} - Network accessline 407
\link{Code}{https://aaai.org/example/guidelines} - Network accessline 408
\link{Datasets}{https://aaai.org/example/datasets} - Network accessline 409
\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 - Network accessline 527
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}, - Dangerous shell operationline 556
AuthorTwo\equalcontrib\textsuperscript{\rm 2}, - Dangerous shell operationline 557
AuthorThree\textsuperscript{\rm 3},\\ - Dangerous shell operationline 558
AuthorFour\textsuperscript{\rm 4}, - Dangerous shell operationline 559
AuthorFive \textsuperscript{\rm 5}} - Dangerous shell operationline 562
\textsuperscript{\rm 1}AffiliationOne,\\ - Dangerous shell operationline 563
\textsuperscript{\rm 2}AffiliationTwo,\\ - Dangerous shell operationline 564
\textsuperscript{\rm 3}AffiliationThree,\\ - Dangerous shell operationline 565
\textsuperscript{\rm 4}AffiliationFour,\\ - Dangerous shell operationline 566
\textsuperscript{\rm 5}AffiliationFive\\ - Network accessline 849
@misc(key, [...] howpublished="\url{http://...}", - Network accessline 850
[...] 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/research-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}, - Network accessline 39
issn = {0036-8075}, URL = {https://science.sciencemag.org/content/208/4447/1019}, eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf}, - Network accessline 40
URL = {https://science.sciencemag.org/content/208/4447/1019}, - Network accessline 41
eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf}, - Network accessline 58
year = {1984}, issn = {0020-7373}, doi = {https://doi.org/10.1016/S0020-7373(84)80003-6}, - Network accessline 59
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}, - Network accessline 61
url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036}, - Network accessline 106
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, - Network accessline 108
howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",
skills/research/research-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- - Network accessline 242
{% \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/research-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) - Network accessline 14
- as a [.zip file](https://github.com/acl-org/acl-style-files/archive/refs/heads/master.zip) - Network accessline 16
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) - Network accessline 46
- 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/research-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/ - Network accessline 3
% % https://github.com/acl-org/acl-style-files/ % - Network accessline 4
% https://github.com/acl-org/acl-style-files/ - Network accessline 35
\ifacl@linenumbers % Add draft line numbering via the lineno package % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/ - Network accessline 36
% 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/research-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 - Network accessline 15
% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets - Network accessline 117
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 - Network accessline 298
You can obtain the complete ACL Anthology as a Bib\TeX{} file from \url{https://aclweb.org/anthology/anthology.bib.gz}.
skills/research/research-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> - Network accessline 2
%%% ... 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, - Network accessline 3
%%% See <https://purl.org/nxg/dist/urlbst> and repository <https://heptapod.host/nxg/urlbst> - Network accessline 125
"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 - Network accessline 126
"[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 - Network accessline 127
"http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED - Network accessline 128
"https://doi.org/" 'doiurl := % prefix to make URL from DOI - Network accessline 129
"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 - Network accessline 130
"https://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref - Network accessline 1191
{ 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 := - Network accessline 1193
{ 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/research-paper-writing/templates/acl/anthology.bib.txt
- Network accessline 10
For example, https://aclanthology.org/N04-1035 can be cited as \cite{galley-etal-2004-whats}. - Network accessline 12
The consolidated file can be downloaded from here: - https://aclanthology.org/anthology.bib - Network accessline 13
- 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 - Network accessline 21
- https://aclanthology.org/anthology-2.bib
skills/research/research-paper-writing/templates/acl/custom.bib
skills/research/research-paper-writing/templates/acl/formatting.md
- Network accessline 10
> https://acl-org.github.io/ACLPUB/ - Network accessline 132
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 - Network accessline 134
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 - Network accessline 252
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 - Network accessline 258
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 - Network accessline 259
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/research-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/research-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/research-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/research-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/research-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/research-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/research-paper-writing/templates/icml2026/fancyhdr.sty
skills/research/research-paper-writing/templates/neurips2025/Makefile
skills/smart-home/openhue/SKILL.md
- Network accessline 13
- IoT - Automation homepage: https://www.openhue.io/cli - Network accessline 14
- Automation homepage: https://www.openhue.io/cli prerequisites: - Network accessline 15
homepage: https://www.openhue.io/cli - Network accessline 41
# 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 42
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 14
[Credential-related source omitted] - Access to secretsline 17
[Credential-related source omitted] - Network accessline 24
- social-media - x-cli homepage: https://github.com/Infatoshi/x-cli - Network accessline 25
- x-cli homepage: https://github.com/Infatoshi/x-cli --- - Network accessline 26
homepage: https://github.com/Infatoshi/x-cli - Network accessline 64
uv tool install git+https://github.com/Infatoshi/x-cli.git - Access to secretsline 82
[Credential-related source omitted] - Access to secretsline 85
[Credential-related source omitted] - Network accessline 88
Get them from: - https://developer.x.com/en/portal/dashboard - Network accessline 89
- https://developer.x.com/en/portal/dashboard - Access to secretsline 95
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 124
[Credential-related source omitted] - Access to secretsline 131
[Credential-related source omitted] - Access to secretsline 132
[Credential-related source omitted] - Access to secretsline 135
[Credential-related source omitted] - Access to secretsline 138
[Credential-related source omitted] - Network accessline 156
x-cli tweet post "hello world" x-cli tweet get https://x.com/user/status/1234567890 - Network accessline 157
x-cli tweet get https://x.com/user/status/1234567890 - Network accessline 225
- 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 227
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: 5 facts
- Passes content downloaded from raw.githubusercontent.com to an interpreterhimalaya/SKILL.md:54High confidence
- Reads a credential store, and the same file sends data over the network or runs a command (line 93)github-auth/SKILL.md:89High confidence
- Reads SSH keys, and the same file sends data over the network or runs a command (line 168)github-auth/SKILL.md:140High confidence
- Reads a credential store, and the same file sends data over the network or runs a command (line 26)github-auth/scripts/gh-env.sh:26High confidence
- Reads a credential store, and the same file sends data over the network or runs a command (line 52)github-issues/SKILL.md:52High confidence
Declared and executed: 3 facts
- Contacts nodejs.org, which SKILL.md, the README and the plugin manifest do not mentionp5js/scripts/setup.sh:25High confidence
- Uses a credential store, which the skill description does not mentiongithub-auth/scripts/gh-env.sh:26High confidence
- Uses a credential store, which the skill description does not mentiongithub-auth/scripts/gh-env.sh:27High confidence
Signal confidence: 492 facts
- Network access in prose of the SKILL.mdclaude-code/SKILL.md:37Low confidence
- Access to secrets in prose of the SKILL.md: a variable, placeholder or call, not a stored valueclaude-code/SKILL.md:42Low confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:113High confidence
- Instruction override signal in code of the SKILL.mdclaude-code/SKILL.md:125Low confidence
- Instruction override signal in code of the SKILL.mdclaude-code/SKILL.md:126Low confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:138High confidence
- Access to secrets in prose of the SKILL.mdclaude-code/SKILL.md:250Low confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:297High confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:298High confidence
- Dangerous shell operation in code of the SKILL.mdclaude-code/SKILL.md:371High confidence
- Dangerous shell operation in code of the SKILL.mdclaude-code/SKILL.md:620High confidence
- Dangerous shell operation in code of the SKILL.mdclaude-code/SKILL.md:653High confidence
- Access to secrets in prose of the SKILL.md: a variable, placeholder or call, not a stored valueclaude-code/SKILL.md:719Low confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:742High confidence
- Instruction override signal in prose of the SKILL.mdclaude-code/SKILL.md:743High confidence
- Access to secrets in prose of the SKILL.mdclaude-code/SKILL.md:752Low confidence
- Broad permissions in prose of the SKILL.mdcodex/SKILL.md:1Low confidence
- Network access in prose of the SKILL.mdcodex/SKILL.md:34Low confidence
- Network access in code of the SKILL.mdcodex/SKILL.md:82High confidence
- Broad permissions in prose of the SKILL.mdopencode/SKILL.md:1Low confidence
- Network access in prose of the SKILL.mdopencode/SKILL.md:35Low confidence
- Access to secrets in prose of the SKILL.md: a variable, placeholder or call, not a stored valueopencode/SKILL.md:47Low confidence
- Network access in code of the SKILL.mdopencode/SKILL.md:180High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:86High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:87High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:89High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:90High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:91High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:92High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:93High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:94High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:96High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:97High confidence
- Network access in prose of the SKILL.mdascii-art/SKILL.md:103Low confidence
- Network access in prose of the SKILL.mdascii-art/SKILL.md:105Low confidence
- Dangerous shell operation in code of the SKILL.mdascii-art/SKILL.md:114High confidence
- Dangerous shell operation in code of the SKILL.mdascii-art/SKILL.md:158High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:181High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:183High confidence
- Dangerous shell operation in code of the SKILL.mdascii-art/SKILL.md:193High confidence
- Dangerous shell operation in code of the SKILL.mdascii-art/SKILL.md:224High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:232High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:233High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:234High confidence
- Dangerous shell operation in code of the SKILL.mdascii-art/SKILL.md:241High confidence
- Network access in prose of the SKILL.mdascii-art/SKILL.md:254Low confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:259High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:294High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:304High confidence
- Network access in code of the SKILL.mdascii-art/SKILL.md:305High confidence
and 442 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Unknown | No evidence | |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Unknown | No evidence | |
| Gemini CLI | Unknown | No evidence | |
| GitHub Copilot | Unknown | No evidence | |
| OpenCode | Unknown | No evidence |
- 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.
