Fine-tuning Gemma 3: A Guide to LoRA, Model Sharding, and Customization

Introducing the New Gemma Python Library

• 🐍 A new JMA Python library is introduced to significantly enhance the user experience for Gemma 3.
• 💬 The library allows users to easily chat with the model, supporting both pre-trained and instruction-tuned versions.
• 🔄 Multi-turn capability is supported, with automatic context remembering and caching for efficient querying.
• 🖼️ Multimodal capabilities are integrated, allowing users to include images directly in prompts for the model to interpret.

Customizing Gemma Models with Cauldron

• 🛠️ Gemma is integrated with Cauldron, an open-source library designed for training arbitrary models on various modalities.
• 📊 The Cauldron API is designed to be familiar, allowing users to create a trainer and launch training with a simple train method.
• 📚 Data set support is flexible, accommodating providers like Hugging Face, TensorFlow Datasets, JSON, or custom wrappers.
• ⚙️ A transform API allows for specific data transformations, such as tokenizing and padding, with high-level transformations provided for common tasks like supervised fine-tuning.
• 🔄 Switching to multimodal data training requires minimal code changes, primarily adding image data and indicating its use to the model.
• 🚀 The optimizer uses Optax, offering standard optimizers and advanced features like gradient clipping and learning rate schedules.

Advanced Features: Sharding and LoRA

• 💻 Model sharding is simplified, allowing users to specify sharding strategies when restoring parameters or during training for datasets and optimizers.
• 🆓 Users can try sharding features for free on public Colab runtimes using TPU v28, which has eight TPU cores.
• 💡 LoRA (Low-Rank Adaptation) is supported for users with single GPUs or limited memory, reducing memory footprint during training.
• 🔒 When using LoRA, the optimizer must also be wrapped in a partial update to ensure only LoRA weights are optimized.
• ✂️ Weight surgery utilities are available to extract LoRA weights or fuse them back into the main model.

Future Plans and Resources

• 🌐 The Gemma website offers more information on features like quantization and DPU examples.
• 🔮 Future plans include adding streaming decoding, model merging (combining capabilities of multiple Gemma experts), and further quantization improvements.
• 🔗 Users are encouraged to check out the Gemma GitHub repository for default trainer configurations and more details.