Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3

This tutorial describes how to customize large language models to better handle tool-calling tasks, which involve models learning to invoke external functions or services. The process uses supervised fine-tuning with specific datasets and frameworks, including parsing multi-turn conversations where models generate and receive tool responses. The work matters because it demonstrates a complete workflow for extracting tool-use patterns from training data, converting them to compatible formats, training the model with specialized techniques, and then measuring how well the model learns to make accurate tool calls.