Train a 4B model to beat Claude Sonnet 4.5 and Gemini Pro 2.5 at tool calling - for free (Colab included)
Analysis
This article discusses the use of DeepFabric, an open-source tool, to fine-tune a small language model (SLM), specifically Qwen3-4B, to outperform larger models like Claude Sonnet 4.5 and Gemini Pro 2.5 in tool calling tasks. The key idea is that specialized models, trained on domain-specific data, can surpass generalist models in specific areas. The article highlights the impressive performance of the fine-tuned model, achieving a significantly higher score compared to the larger models. The availability of a Google Colab notebook and the GitHub repository makes it easy for others to replicate and experiment with the approach. The call for community feedback is a positive aspect, encouraging further development and improvement of the tool.
Key Takeaways
“The idea is simple: frontier models are generalists, but a small model fine-tuned on domain-specific tool calling data can become a specialist that beats them at that specific task.”