We Got Claude to Fine-Tune an Open Source LLM

We gave Claude the ability to fine-tune language models using a new tool called Hugging Face Skills . Not just write training scripts, but to actually submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. This tutorial shows you how it works and how to use it yourself.

Claude Code can use "skills"—packaged instructions, scripts, and domain knowledge—to accomplish specialized tasks. The hf-llm-trainer skill teaches Claude everything it needs to know about training: which GPU to pick for your model size, how to configure Hub authentication, when to use LoRA versus full fine-tuning, and how to handle the dozens of other decisions that go into a successful training run.

The model trains on Hugging Face GPUs while you do other things. When it's done, your fine-tuned model appears on the Hub, ready to use.

This isn't a toy demo. The skill supports the same training methods used in production: supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards. You can train models from 0.5B to 70B parameters, convert them to GGUF for local deployment, and run multi-stage pipelines that combine different techniques.

This is your chance to adjust anything. Change the output repo name, pick different hardware, or ask Claude to modify training parameters. Once you approve, the agent submits the job.

For models larger than 3B parameters, the agent automatically uses LoRA (Low-Rank Adaptation) to reduce memory requirements. This makes training 7B or 13B models feasible on single GPUs while preserving most of the quality of full fine-tuning.

GRPO is a reinforcement learning task that is proven to be effective on verifiable tasks like solving math problems, writing code, or any task with a programmatic success criterion.

Let's walk through a complete example. We'll fine-tune a small model to see the full workflow, then explore more advanced capabilities.

Use SFT when you have high-quality examples of the behavior you want. Customer support conversations, code generation pairs, domain-specific Q&A—anything where you can show the model what good looks like.

DPO is sensitive to dataset format. It requires columns named exactly chosen and rejected , or a prompt column with the input. The agent validates this first and shows you how to map columns if your dataset uses different names.

Always run a demo before committing to a multi-hour production job. A $0.50 demo that catches a format error saves a $30 failed run.

Hugging Face skills are compatible with Claude Code, Codex, and Gemini CLI. With integrations on the way for Cursor, Windsurf, and Continue.

You have to authenticate to your Hugging Face account with a write-access token so that the job can create a model repo.

Configure Hugging Face MCP Server to use your write token by sending it in either the HF_TOKEN or Authorization: Bearer HTTP Headers.