Cormac Brick

Fine-Tuning Pushed FunctionGemma From 46% to 90% Function-Calling Accuracy

Cormac Brick, a Google AI Edge engineer, argues that on-device agents are becoming practical when developers either use system models such as Gemini Nano through Android AI Core or ship narrow, fine-tuned tiny models with LiteRT-LM. His main example is FunctionGemma, a 270 million parameter function-calling model that rose from about 46% accuracy out of the box to more than 90% on most tested app-intent functions after synthetic-data fine-tuning. Brick presents the tradeoff plainly: system GenAI is easier when it fits, while app-shipped tiny models require more work but can run locally, offline, and with more control.