This repository provides a collection of guides and examples for Google's Gemma family of open AI models, targeting developers and researchers. It aims to simplify the adoption and utilization of Gemma models for various text and vision-based AI tasks, offering practical implementations and use cases.

How It Works

The cookbook offers a structured approach to interacting with Gemma models, providing runnable notebooks that demonstrate specific functionalities. It covers a range of Gemma variants, including those for coding (CodeGemma), vision-language tasks (PaliGemma), and safety evaluation (ShieldGemma), showcasing their capabilities through practical code examples.

Quick Start & Requirements

• Installation typically involves cloning the repository and running provided notebooks, often requiring Python environments with libraries like TensorFlow or PyTorch.
• Specific model weights and potential hardware acceleration (e.g., GPUs) may be necessary depending on the notebook's complexity.
• Refer to individual notebook instructions for precise dependencies and setup.
• Gemma Models on Hugging Face
• Gemma on Kaggle
• Gemma on Google Cloud Vertex AI

Highlighted Details

• Comprehensive coverage of Gemma model variants: Gemma, CodeGemma, PaliGemma, RecurrentGemma, ShieldGemma, DataGemma.
• Notebooks demonstrating end-to-end use cases and integration with Google Cloud Platform.
• Examples for both text generation and vision-language tasks.
• Community support via developer forums and GitHub issues for questions and feature requests.

Maintenance & Community

• Actively maintained by Google, with contributions welcomed from the community.
• Community engagement channels include a developer forum and GitHub issues.
• Roadmap and contribution guidelines are available within the repository.

Licensing & Compatibility

• The Gemma models themselves are subject to their own terms of service and licenses, which are generally permissive for research and commercial use but may have specific restrictions.
• The cookbook code is typically provided under a permissive license (e.g., Apache 2.0), allowing broad usage and modification.

Limitations & Caveats

• The effectiveness and resource requirements of the examples are dependent on the specific Gemma model version used and the underlying hardware.
• Some advanced use cases or larger models may necessitate significant computational resources, including powerful GPUs.