Gemma explained: An overview of Gemma model family architectures

Gemma is a family of lightweight, open models developed using the same foundational research as the Gemini models. It includes various architectures tailored for specific use cases, such as single modality (text input/output), coding specialization, and multi-modality (text and image input/output). The models vary in size to accommodate different hardware and inference requirements. Key models in the Gemma family include Gemma 1, CodeGemma, Gemma 2, RecurrentGemma, and PaliGemma, each designed for distinct functionalities. The architecture is based on a transformer decoder, allowing for efficient text generation and processing of up to 8192 tokens. The models utilize advanced techniques like multi-head attention and novel activation functions to enhance performance. CodeGemma, specifically, is optimized for code completion tasks, trained on a vast dataset of code tokens. The guide emphasizes that it assumes prior knowledge of neural networks and transformers, providing resources for those needing a refresher. Future articles will delve into the latest model, Gemma 2, which promises improved safety and performance.

- Gemma models are designed for various use cases, including text and code generation.

- The architecture is based on transformer decoders, allowing for efficient processing of long sequences.

- CodeGemma is specialized for coding tasks, trained on over 500 billion tokens of code.

- The guide assumes familiarity with AI concepts and provides resources for further learning.

- Upcoming posts will explore enhancements in the new Gemma 2 model.