Fine-Tuning LLMs for Tool Use with Example Code

Learn to fine-tune the Gemma-3-1b-it model for tool use through a comprehensive 26-minute tutorial that combines conceptual understanding with hands-on Python implementation. Explore the fundamentals of fine-tuning and training data requirements before diving into a practical five-step process: defining tools, generating queries, creating and refining traces, fine-tuning the model, and evaluating performance. Follow along with concrete code examples that demonstrate how to enhance large language models' ability to interact with external tools, with access to supporting materials including a GitHub repository, dataset, and the resulting fine-tuned model for further experimentation.