How Google Uses Transformer

The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.

The article discusses the deployment of on-device generative AI (GenAI) using LiteRT-LM in Chrome, Chromebook Plus, and Pixel Watch.

The article introduces T5Gemma, a new collection of encoder-decoder models derived from pretrained decoder-only models.

The article introduces Gemma 3n, a mobile-first architecture designed for on-device AI, highlighting its multimodal capabilities and architectural innovations.

LiteRT is a new API designed to simplify and enhance AI model performance on mobile devices by leveraging GPU and NPU acceleration.

The article discusses the Gemma 3 1B model, a lightweight language model designed for mobile and web applications using Google AI Edge.

The article explores the RecurrentGemma architecture, a hybrid model that combines gated linear recurrences with local sliding window attention, enhancing performance for long context prompts.

The article provides an overview of the Gemma model family architectures, detailing its lightweight, state-of-the-art open models derived from Gemini research.

This article provides a comprehensive guide on using Gemma with Ray on Vertex AI, detailing the steps to set up, fine-tune, and deploy machine learning models.

The article discusses the release of the Gemma 2 model with 27 billion parameters, highlighting its capabilities in Keras and integration with JAX for efficient model training.

The article introduces the expansion of the Gemma family with two new models, CodeGemma and RecurrentGemma, designed specifically for developers and researchers.

The article introduces Gemma models in Keras, a family of lightweight, state-of-the-art open models that leverage the same technology as the Gemini models.