Extending FunctionGemma for Practical On-Device Mobile Function Calling

Abstract:On-device assistants require function-calling models that map natural language to local system actions, but existing resources emphasize web APIs or narrow mobile-action catalogs. We extend FunctionGemma 270M-it to practical Android workflows by introducing MOBILEACTIONSEXTENDED, a synthetic, schema-validated dataset of ~9,500 conversations covering fifteen device-control categories, including messaging, phone calls, camera/screenshot, brightness control, device-status queries, flashlight control, and application management. We fine-tune the 270M model with TRL supervised fine-tuning under completion-only loss, producing an extended specialist and a combined model trained jointly with Google's MOBILEACTIONSGOOGLE. On MOBILEACTIONSEXTENDED, end-to-end accuracy improves from 29.3% for the base model and 17.2% for Google's Mobile-Actions variant to 76.5%. The combined model retains 76.5% on MOBILEACTIONSEXTENDED and reaches 82.3% on MOBILEACTIONSGOOGLE, down from the 90.3% of Google's Mobile-Actions specialist, representing an 8.0-percentage-point trade-off in return for doubling category coverage. We release the dataset, fine-tuned models, reproducible training/evaluation pipeline, and an Android demo, highlighting compact local function calling as a practical path towards low-latency and privacy-preserving mobile assistants.

Accepted at EMNLP 2026. 16 pages, 1 figure, 8 tables. Includes main paper, references, and appendices. Code, datasets, models, and demo available at GitHub and Hugging Face

Machine Learning (cs.LG); Software Engineering (cs.SE)

arXiv:2609.25373 [cs.LG]

(or arXiv:2609.25373v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.25373 arXiv-issued DOI via DataCite (pending registration)