Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3
How to fine-tune tool-calling LLMs: a working blueprint for agents that actually use external APIs.
Why it matters
Practitioners building agentic systems need tool-calling capability. This is a hands-on guide to adapting open models (Qwen3, Aquila) for reliable function calling — directly applicable to production deployments.
The key facts
10 to knowEnd-to-end fine-tuning pipeline for tool-calling LLMs
LoRA adaptation using PyTorch for efficiency
Qwen3 and XYZ-Aquila-SFT models covered
ChatML rendering for tool-call formatting
Trajectory parsing and structured extraction techniques
Tutorial covers end-to-end fine-tuning pipeline for tool-calling
Focuses on XYZ-Aquila-SFT and Qwen3 models
Covers trajectory parsing, structured tool-call extraction, ChatML rendering, LoRA adaptation
Published MarkTechPost (vendor tutorial / how-to format)
PyTorch-based implementation
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: 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.
