Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3 — 2026-08-15

## Short Segments Welcome to Impact Vector, where we dive into the latest in AI tools and technology. Today, we're exploring a comprehensive guide to fine-tuning tool-calling language models using XYZ-Aquila-SFT and Qwen3. This feature story will take you through the practical steps and implications of implementing an end-to-end supervised fine-tuning pipeline. Stay tuned as we unpack the details and what it means for developers and AI practitioners. ## Feature Story Fine-tuning tool-calling language models just got more accessible with a detailed guide using XYZ-Aquila-SFT and Qwen3. This tutorial provides an end-to-end supervised fine-tuning pipeline, leveraging the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. The process begins with streaming and inspecting the dataset, parsing multi-turn tool-use trajectories, and extracting structured tool calls. This step is crucial for analyzing corpus characteristics and preserving embedded reasoning and observation patterns. One of the key tasks involves converting tool schemas between message-embedded and structured formats. This conversion is essential for rendering Qwen-compatible ChatML with assistant-only loss masking. The guide also covers preparing a custom PyTorch dataset and collator, which are pivotal for fine-tuning the Qwen3-0.6B model with LoRA. This approach allows for a more efficient and targeted training process, enhancing the model's ability to predict tool calls accurately. After the fine-tuning process, the tutorial evaluates tool-call prediction before and after training. This evaluation is critical for understanding the improvements and adjustments made during the fine-tuning process. The transformed dataset and corpus statistics are then exported for further experimentation, providing a robust foundation for future developments and applications. The rise of AI agents and tool-enabled applications has made function calling a critical capability for language models. While proprietary models like GPT-4 excel at function calling out of the box, open-source alternatives require specialized fine-tuning to achieve comparable performance. This guide addresses that gap, offering a practical solution for developers working with open-source models. In the broader context, fine-tuning open-source models for function calling is becoming increasingly important. As AI agents are deployed in production environments, their ability to query databases, trigger workflows, retrieve real-time data, and act on a user's behalf is paramount. However, base models often struggle with hallucinating tools, passing incorrect parameters, and attempting actions without proper clarification. These issues can erode trust and hinder production deployment. By following this guide, developers can enhance the reliability and accuracy of their AI models, making them more suitable for real-world applications. The use of serverless model customization, as mentioned in related contexts, further accelerates agentic tool calling, providing a scalable and efficient solution for AI practitioners. In conclusion, this comprehensive guide to fine-tuning tool-calling language models using XYZ-Aquila-SFT and Qwen3 offers a valuable resource for developers and AI practitioners. By implementing the steps outlined in the tutorial, users can improve the performance and reliability of their AI models, paving the way for more effective and trustworthy AI applications in production environments.