Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3
| Source: MarkTechPost
Tags: Qwen3, LoRA, fine-tuning, tool-calling, PEFT, XYZ-Aquila-SFT
MarkTechPost walks through a complete supervised fine-tuning pipeline for Qwen3-0.6B using the XYZ-Aquila-SFT dataset and LoRA, covering tool-call parsing, ChatML formatting, and pre/post-training evaluation — a practical starting point for teams building tool-use agents.
Details
This tutorial details an end-to-end SFT pipeline for enabling tool-calling behavior in Qwen3-0.6B, using the XYZ-Aquila-SFT dataset from Hugging Face. The guide covers dataset streaming, multi-turn trajectory parsing, structured tool-call extraction, and conversion between message-embedded and structured tool schemas. Training uses LoRA (r=16) via PEFT with assistant-only loss masking — a standard technique to prevent the model from learning to predict user or system tokens. The tutorial sets max sequence length at 2,048 tokens and runs for 30 steps with a cosine learning rate schedule and gradient accumulation of 8. The evaluation section compares tool-call prediction accuracy before and after fine-tuning, though specific benchmark numbers are not disclosed in the article. The source exports the transformed dataset and corpus statistics for further experimentation. This is a practitioner-level walkthrough for teams building function-calling or agent pipelines who need to adapt smaller, cost-efficient models. The techniques are well-established, but combining Qwen3 and XYZ-Aquila-SFT in one reproducible guide provides hands-on value.