在Claude中微调LLM：Hugging Face工作室

AI engineers are going to love this!

I built a @huggingface fine-tuning studio that lets you fine-tune any LLM directly from Claude.

The app connects to the HF Hub for model and dataset search. It handles chat template formatting for the training data, and lets you configure LoRA rank, quantization, batch size, and learning rate directly from Claude.

Training runs on HF's GPU infra via AutoTrain.

Once training finishes, you can also chat with your fine-tuned model (or any other LLM on HF) directly from Claude.

The studio's built with @manufact's mcp-use SDK, an open-source full-stack framework to build MCP Apps for Agents.

The video below depicts my fine-tuning studio in action and I worked with the manufact team to put this together.

In mcp-use, any MCP tool can be associated with a UI.

You define a tool handler, create a React component, and the mcp-use framework handles the tool registration, prop mapping between server and widget, bundling, and hot reload during development.

The widgets follow the MCP Apps standard, inspired by OpenAI's Apps SDK.

Claude is an example, but you can render them as interactive UI elements in any conversational MCP client that supports it.

Similarly, this pattern works for any workflow you want to bring inside a chat, like eval dashboards, dataset explorers, or model comparison tools.