FunctionGemma is a small yet powerful language model. It enables developers to create fast and cost-effective agents that convert natural language into actionable API calls. Thanks to its lightweight design, it works exceptionally well on edge devices, where speed and efficiency are crucial.
Takeaways 🚀
✅Simplify FunctionGemma fine-tuning process
✅Leverage Tunix for TPU efficiency
✅Achieve faster, cost-effective deployments
✅Boost accuracy with minimal training
✅Optimize models for edge devices
Previously, in the blog “A Guide to Fine-Tuning FunctionGemma”, we discussed how to fine-tune FunctionGemma using the Hugging Face TRL library on GPUs. Now, we’ll explore a different approach. This guide will show you how to fine-tune FunctionGemma using Google Tunix on TPUs. With Tunix, the process becomes simpler and more efficient.
What is Tunix?
To begin with, Tunix is a lightweight library built with JAX. It simplifies the post-training process for large language models (LLMs). Moreover, it supports a variety of fine-tuning methods, such as:
- Supervised fine-tuning
- Parameter-efficient fine-tuning
- Preference tuning
- Reinforcement learning
- Model distillation
In addition, Tunix works seamlessly with popular open models like FunctionGemma, Qwen, and LLama. It is also optimized for high efficiency across various hardware accelerators, including Google TPUs.
How to Fine-Tune FunctionGemma with Tunix
Fine-tuning FunctionGemma with Tunix is straightforward. Even better, you can use a free-tier Colab TPU v5e-1 to get started. For this tutorial, we’ll use the Mobile Action dataset, which was also featured in the earlier GPU-based guide.
Key Steps:
- Download Model Weights and Dataset
First, access FunctionGemma’s model weights and dataset through the Hugging Face Hub. - Set Up Tunix
Next, configure Tunix. It uses JAX sharding schemes for parallelism. However, since TPU v5e-1 is single-core, you’ll create a simple mesh without sharding. - Apply LoRA Adapters
Then, use the Qwix library to apply LoRA adapters to the model’s attention layers. - Define a Custom Dataset Class
After that, create a custom dataset class to support completion-only loss. This step ensures compatibility with Tunix. - Generate Data
Once the dataset class is ready, build data generators to feed training data into Tunix. - Start Fine-Tuning
Finally, begin the fine-tuning process. Tunix achieves high TPU utilization and significantly improves accuracy after just one epoch.
Why Use Tunix on TPUs?
There are several reasons why Tunix is an excellent choice for fine-tuning on TPUs:
- Efficiency: Tunix maximizes TPU utilization, even on free-tier hardware.
- Performance: It delivers noticeable accuracy improvements with minimal training time.
- Flexibility: Tunix supports various fine-tuning techniques and works seamlessly with different hardware setups.
Exporting the Fine-Tuned Model
Once you’re satisfied with the results, you can merge the LoRA adapters and export the fine-tuned model to safetensors. This allows you to use the model for downstream tasks, such as on-device deployment with LiteRT.
In summary, Tunix makes fine-tuning FunctionGemma for specific tasks both easy and efficient. Its modular design, speed, and flexibility make it a valuable tool for developers. Whether you’re working on edge devices or large-scale systems, Tunix helps you optimize your models with minimal effort.
To learn more, check out the Tunix documentation and follow the Tunix repository for updates.
