How Google Uses JAX

This tutorial demonstrates how to fine-tune FunctionGemma, a small language model for translating natural language into API calls, using Google's Tunix library on TPUs.

LiteRT has evolved from its TensorFlow Lite foundation into a universal on-device AI inference framework, now offering production-ready GPU acceleration across six platforms and streamlined NPU int...

This article demonstrates how to fine-tune FunctionGemma, a specialized 270M parameter Gemma 3 model designed for function calling in agentic AI systems.

This article serves as a practical guide for developers working with JAX on Cloud TPUs, focusing on essential tools and techniques for debugging and profiling machine learning workflows.

The article introduces A2UI, an open-source project designed for agent-driven interfaces that allows agents to generate contextually relevant user interfaces.

The article discusses the advancements in on-device AI powered by MediaTek's Neural Processing Unit (NPU) and the introduction of the LiteRT NeuroPilot Accelerator.

The article discusses optimizing performance on Qualcomm's Neural Processing Unit (NPU) using LiteRT, Google's high-performance on-device ML framework.

The article discusses the JAX AI Stack, a modular framework for building production AI models on Google Cloud TPUs.

Metrax is a high-performance library designed for efficient and robust model evaluation metrics in JAX, addressing the need for standardized metrics during the migration from TensorFlow.

Agent Garden is a platform designed to facilitate the development and deployment of AI agents, making it accessible to all users, not just those on Google Cloud.

The article introduces Coral NPU, a full-stack, open-source platform designed to enhance Edge AI capabilities on low-power devices.

The article discusses building high-performance data pipelines using Grain, a data loading library for JAX, and ArrayRecord, an efficient file format.

The article introduces Tunix, a new open-source, JAX-native library designed for post-training of large language models (LLMs).

The article discusses how JAX, a popular framework for AI model development, is being increasingly adopted in scientific computing, particularly for solving complex Partial Differential Equations (...

The article introduces Gemma 3 270M, a compact AI model designed for hyper-efficient task-specific fine-tuning.

This article provides a comprehensive guide on how to train a GPT-2 model using JAX on TPU, highlighting the ease of leveraging Google TPUs for free.

The article discusses the increasing adoption of JAX in robotics, highlighting its efficiency in optimal control and simulation. It features insights from Max Muchen Sun, a Robotics Ph. D.

Stanford's Marin project introduces the first fully open foundation model developed using JAX, emphasizing transparency in the scientific process behind AI models.

The article discusses how to use KerasHub for loading model weights from SafeTensors into Keras, enabling flexible end-to-end machine learning workflows across different frameworks like JAX, PyTorc...

The article introduces Keras Recommenders, a new library designed to simplify the creation of state-of-the-art recommendation systems using Keras with JAX, TensorFlow, or PyTorch.

Gemma 3 is the latest version of the Gemma open-model family, boasting enhanced capabilities such as multimodality, longer context windows, and improved reasoning.

The article discusses the launch of ShieldGemma 2, a safety content classifier model built on Gemma 3, aimed at detecting harmful content in both synthetic and natural images.

PaliGemma 2 mix is an advanced vision-language model designed for multiple tasks, allowing developers to utilize a single model for various applications such as image captioning, object detection, ...

PaliGemma 2 is the latest vision-language model from Google, designed to simplify the process of building advanced AI that can interpret visual inputs.

The article announces that Francois Chollet, the creator of Keras, is leaving Google to pursue new opportunities.

The article introduces Keras Hub, a unified library for pretrained models that simplifies access to both natural language processing (NLP) and computer vision (CV) architectures.

The article discusses the PaliGemma architecture, a lightweight open vision-language model (VLM) inspired by PaLI-3.

LiteRT, formerly known as TensorFlow Lite, is a high-performance runtime for on-device AI that now supports models from multiple frameworks including PyTorch, JAX, and Keras.

The article discusses the release of Gemma 2, a new suite of open models that sets a new standard for performance and accessibility in conversational AI.

The article discusses the advancements in responsible AI through the introduction of Gemma 2, which includes models with 27 billion and 9 billion parameters, emphasizing safety and accessibility.

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.

Model Explorer is a powerful graph visualization tool designed to simplify the development and optimization of machine learning models for edge devices.

The article recaps the Google I/O 2024 event, highlighting advancements in AI technologies aimed at making AI accessible for developers.

The article introduces PaliGemma, an open vision-language model, along with Gemma 2, the next generation of the Gemma models, and updates to the Responsible AI Toolkit.

This article discusses how to publish Keras models on Kaggle and Hugging Face, highlighting the ease of sharing fine-tuned models with the community.

The article reveals the program lineup for Google I/O, highlighting key sessions and events for developers.

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.

The Women in Machine Learning Symposium 2023 is set to take place on December 7, 2023, featuring a diverse lineup of female presenters who will discuss advancements in AI/ML and their practical app...

The article highlights the achievements and activities of Google Machine Learning communities in the second quarter of 2023, showcasing various training campaigns, community events, and innovative ...

The article provides an overview of the upcoming Google I/O event, highlighting key updates across Google's developer products, solutions, and technologies.

The Google Dev Library Newsletter: 20th Edition highlights innovative projects developed using Google technologies, showcasing contributions to the Google Dev Library platform.

The article discusses the launch of the PaLM API and MakerSuite, tools designed to simplify the development of generative AI applications.

This article discusses the migration process from App Engine Users to Cloud Identity Platform, highlighting the benefits of moving to standalone cloud services.

This article provides a comprehensive guide on using the App Engine Users service, focusing on user authentication and management within Google Cloud's App Engine.

This article discusses the migration from App Engine pull tasks to Cloud Pub/Sub, emphasizing the benefits of moving to a more portable and flexible messaging system.

This article provides a comprehensive guide on using App Engine pull tasks, focusing on how to implement pull queues in a sample application.

The article discusses the extension of support for App Engine bundled services in its 2nd-generation runtimes, emphasizing the migration process for Python applications.

This article discusses the migration process from App Engine Memcache to Cloud Memorystore, specifically focusing on Redis as the caching engine.

The article discusses how App Engine users can leverage Cloud Functions to modernize their applications by refactoring monolithic apps into microservices.