Complete Guide to Fine-Tuning Tool-Calling LLMs with Qwen3

MarkTechPost has published a hands-on guide covering the end-to-end process of fine-tuning large language models for tool-calling tasks, using the XYZ-Aquila-SFT dataset alongside Qwen3. The guide walks through dataset preparation, supervised fine-tuning (SFT) setup, and evaluation of tool-use accuracy, making it directly applicable to developers building agentic pipelines. Tool-calling reliability is a critical bottleneck in production agentic systems, and targeted fine-tuning can significantly improve function-calling precision over base or instruction-tuned models. Developers working on agents that invoke APIs, databases, or external services will find this directly actionable for improving call accuracy and reducing hallucinated arguments. Qwen3's strong baseline tool-use capabilities make it a practical starting point for teams looking to specialize a model for their own tool schemas.