A recent tutorial provides a comprehensive framework for fine-tuning language models to perform tool-calling tasks. The guide covers the necessary steps for parsing trajectories, extracting structured tool calls, and adapting the models using PyTorch. It also discusses rendering ChatML and implementing efficient LoRA adaptation. This tutorial is intended to help developers and researchers fine-tune their language models for specific tool-calling applications. The fine-tuning of language models has significant implications for the development of more accurate and efficient AI-powered tools.