Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3
A new tutorial describes a workflow for adapting large language models to handle tool calls using Qwen3 and LoRA. It covers trajectory parsing and structured extraction for building agent systems.
Recent technical documentation presents a structured approach for modifying large language models to perform tool interactions. The guide focuses on creating a fine-tuning pipeline that enables models to interact with external APIs and functions reliably.
The approach leverages Qwen3 as the foundation while applying Low-Rank Adaptation to optimize training efficiency. It also addresses specific formatting requirements, such as ChatML rendering, to ensure compatibility with existing inference frameworks.
These skills are essential for constructing AI agents capable of autonomous decision-making. By providing structured extraction methods, developers can build more robust systems capable of handling complex, multi-step tasks without manual intervention.
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