MCP Agent Fine tuning Workshop - Ronan McGovern Workshop Overview 00:00
Ronan McGovern introduces the workshop on fine-tuning MCP agents, detailing objectives and outcomes.
The goal is to generate high-quality reasoning traces, save logs, fine-tune a model (specifically Quen 3), and evaluate performance improvements.
Introduction to MCP 01:02
MCP (Model Context Protocol) facilitates LLMs' access to tools, such as a web browser.
It serves as a repository of tool information and manages tool execution, returning results to the LLM.
An API endpoint will expose the language model, requiring conversions between MCP and OpenAI formats.
Data Collection and Model Setup 05:14
Users must clone the provided repository and navigate to the MCP agent fine-tune folder for instructions.
Emphasis on using a consistent model for data generation and fine-tuning, recommending a 30 billion parameter Quen model.
The setup requires defining a Docker image, enabling reasoning, and specifying tool parsers for tool calls.
Running the Agent 10:19
The agent is run to perform tasks like navigating to a website and retrieving specific information.
Logging features capture both user messages and tool interactions needed for fine-tuning.
The importance of generating multiple high-quality traces is highlighted for effective model training.
Trace Adjustment and Saving 13:46
Users are encouraged to clean up traces by merging or modifying logs to ensure quality training data.
Pushing the cleaned data to the Hugging Face hub for fine-tuning is discussed, focusing on unrolling multiple interaction turns for better training efficacy.
Fine-Tuning Process 22:51
Introduction to the fine-tuning notebook setup, including installing necessary libraries and preparing model parameters.
Discussion on applying low-rank adapters (LoRA) to the model focuses on specific matrices to enhance training efficiency.
Training and Evaluation 29:10
The training process involves testing the model's performance before and after fine-tuning, noting improvements in task execution.
Future recommendations include using a larger dataset and experimenting with reinforcement learning for further enhancements.
Conclusion and Resources 35:01
The workshop concludes with encouragement to experiment with trace generation and fine-tuning.
Resources and additional materials are available in the repository, along with suggested videos for deeper understanding of MCP setup and customization.