A Guide to Fine-Tuning FunctionGemma- Google Developers Blog

This post introduces FunctionGemma, a Gemma 3 270M model fine-tuned for function calling, and demonstrates how to fine-tune it to resolve tool selection ambiguity. It details a case study using the `bebechien/SimpleToolCalling` dataset to differentiate between `search_knowledge_base` and `search_google` tools. A critical note on data distribution emphasizes the need for pre-mixed training data to avoid catastrophic performance. The post also introduces the FunctionGemma Tuning Lab, a no-code demo tool for fine-tuning.