Ronan McGovern

This workshop details the process of fine-tuning an AI agent that utilizes the Model Context Protocol (MCP) for tool access. It covers generating high-quality reasoning traces from agent interactions with tools, saving these multi-turn conversations, and using them to fine-tune a language model, specifically demonstrating with a Quen model. The goal is to improve the agent's performance by training it on its own successful interactions.

This workshop details the process of preparing data and fine-tuning a text-to-speech (TTS) model, specifically Sesame's CSM 1B model, to produce speech that mimics a target voice. It covers extracting audio from sources like YouTube, transcribing it, and formatting it into a dataset suitable for training. The process leverages the Unsloth library for efficient fine-tuning and demonstrates how to evaluate the model's performance before and after the fine-tuning process.