• FunctionGemma is a powerful small language model that enables developers to ship fast and cost-effective agents that can translate natural language into actionable API calls, especially on edge devices. • In the previous A Guide to Fine-Tuning FunctionGemma blog, our colleague shared some best practices for finetuning FunctionGemma using the Hugging Face TRL library on GPUs. • In this post we are going to explore a different path by using Google Tunix to perform the finetuning on TPUs. • You can find the complete notebook here. • Tunix is a lightweight library implemented in JAX and designed to streamline the post-training of Large Language Models (LLMs) and it is part of the extended JAX AI Stack. • Tunix supports a wide range of modern LLM post-training techniques such as supervised finetuning, Parameter-Efficient Fine-Tuning, preference tuning, reinforcement learning, and model distillation.

Article Summaries:

  • Easy FunctionGemma finetuning with Tunix on Google TPUs

A new tutorial demonstrates how to fine‑tune the FunctionGemma 270 M‑parameter language model on Google’s free‑tier Colab TPUs using the JAX‑based Tunix library. Unlike the earlier guide that employed Hugging Face’s TRL on GPUs, this approach applies LoRA‑based supervised fine‑tuning on the Mobile Action dataset. Tunix handles model loading from safetensors, sharding, and LoRA adapter insertion, while a custom dataset class supplies completion‑only loss data. The complete notebook is publicly available, offering developers a lightweight, cost‑effective path to deploy FunctionGemma on edge devices.

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