Fine-Tuning Tool-Calling LLMs with Qwen3 and XYZ-Aquila-SFT
A complete guide on fine-tuning Qwen3-0.6B using the XYZ-Aquila-SFT dataset and LoRA on a Colab-compatible GPU to enhance tool usage performance.
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- Complete pipeline for fine-tuning large language models in tool utilization.
- Leverages XYZ-Aquila-SFT dataset and LoRA on resource-efficient hardware.
- Performs comparative evaluations using teacher-forced metrics.
- Exports reusable model adapters and structured analytical records.
Enhancing the capability of large language models to interact seamlessly with external tools represents a vital step toward advanced agentic AI. This comprehensive guide outlines a practical workflow for analyzing, transforming, fine-tuning, and evaluating complex tool-use trajectories sourced from the XYZ-Aquila-SFT dataset, ensuring a robust framework for developers.
A core strategy in this pipeline involves preserving the original conversational structure while applying token-level supervision exclusively to assistant responses. Furthermore, the implementation utilizes LoRA to adapt the Qwen3-0.6B model efficiently, making the entire training process compatible with standard Colab-friendly GPU environments without demanding excessive computational power.
Applying LoRA to a compact model like Qwen3-0.6B highlights current industry shifts toward resource-efficient AI development. By restricting supervision to specific output tokens, developers can prevent structural degradation of the base model's logic while tailoring its behavior for specialized API or tool-calling tasks.
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To measure the effectiveness of the pipeline, the team conducted performance comparisons between baseline and post-training tool execution using teacher-forced evaluation. The process concludes by exporting reusable structured records, model adapters, and detailed analytical statistics for future iterations.
Ultimately, this workflow establishes a solid foundation for scaling supervised fine-tuning in agentic models, experimenting with sequence-length policies, and building highly capable AI assistants. This technical overview was prepared by Sana Hassan, a dual-degree student at IIT Madras and consulting intern at Marktechpost.
Source: MarkTechPost
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