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This tutorial provides a practical implementation of supervised fine-tuning for tool-calling capabilities in Qwen3 models using LoRA and PyTorch. It details essential preprocessing steps such as parsing multi-turn trajectories, converting embedded schemas to structured formats, and applying assistant-only loss masking within the ChatML framework. The guide is particularly relevant for developers seeking efficient methods to adapt open-source LLMs for specific API interaction tasks without full model retraining.

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

MarkTechPostAugust 15, 20263 min read

Implement an end-to-end fine-tuning pipeline for tool-calling language models. This tutorial covers parsing trajectories, structured tool-call extraction, Qwen-compatible ChatML rendering, and efficient LoRA adaptation using PyTorch. The post Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3 appeared first on MarkTechPost.

In this tutorial, we implement an end-to-end supervised fine-tuning pipeline for the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. We stream and inspect the dataset, parse multi-turn tool-use trajectories, extract structured tool calls, analyze corpus characteristics, and preserve embedded reasoning and observation patterns. We then convert tool schemas between message-embedded and structured formats, render…

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