Individual skill
tinker-fine-tuning
Provides guidance for fine-tuning LLMs using the Tinker cloud training API from Thinking Machines Lab. Use when running supervised fine-tuning, reinforcement learning (GRPO/PPO), or LoRA training on cloud GPUs via Tinker's managed infrastructure instead of local compute.
backend/cli/skills/cloud-compute/tinker/SKILL.md
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- Last checked
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- fine-tuning Declared skill tag
- tinker Declared skill tag
- lora Declared skill tag
- reinforcement-learning Declared skill tag
- supervised-learning Declared skill tag
- dpo Declared skill tag
- rlhf Declared skill tag
- cloud-training Declared skill tag
- vision-language-models Declared skill tag
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# Tinker API - Cloud LLM Fine-Tuning
Expert guidance for fine-tuning large language models using Tinker's managed cloud training API. Tinker handles GPU allocation, model hosting, and distributed training — you write the training logic, Tinker runs it on cloud infrastructure.
## When to Use This Skill
**Use Tinker when you need to:**
- Fine-tune models up to 235B parameters without managing GPU infrastructure
- Run LoRA training on Qwen, Llama, DeepSeek, or GPT-OSS models
- Train vision-language models (Qwen3-VL)
- Implement custom RL loops (GRPO, PPO, importance sampling) on cloud GPUs
- Iterate quickly with a training API that handles hardware provisioning
**Do NOT use Tinker when:**