How Google Uses Hugging Face

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.

The article discusses how to fine-tune the Gemma 3 270M model for on-device applications, enabling developers to create custom AI models without the need for expensive hardware.

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 launch of the Google AI Edge Gallery app, which now includes audio capabilities and is available on Google Play.

This article discusses the integration of Google's EmbeddingGemma model with Google Cloud's Dataflow to create a scalable embedding pipeline for AI applications.

EmbeddingGemma is an innovative open embedding model designed for on-device AI applications, featuring 308 million parameters for efficient performance.

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

LangExtract is an open-source Python library powered by Gemini, designed to facilitate the extraction of structured information from unstructured text.

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 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.

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 discusses the expansion of Google's AI Edge platform to support on-device small language models (SLMs) with multimodal capabilities, including the introduction of the Gemma 3 and Gemma ...

The article discusses the launch of Gemma 3, a state-of-the-art AI model optimized for consumer GPUs through Quantization-Aware Training (QAT).

TxGemma is a collection of open models designed to enhance the efficiency of therapeutic development by utilizing large language models.

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

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, ...

The article reflects on the first year of the Gemini era, highlighting the advancements made in AI technology and the community's contributions.

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 Web AI Summit 2024, hosted by Google on October 18, 2024, focused on client-side AI for developers, showcasing how machine learning models can operate offline in web browsers.

The article discusses the expansion of the Responsible Generative AI Toolkit, introducing new tools designed for various large language models (LLMs) like Gemma and Gemini.

The article discusses Google's advancements in multilingual AI through the introduction of Gemma 2, a lightweight model designed to empower communities to adapt AI for their languages.

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

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 presents three engaging project ideas utilizing Gemma, a family of open models for AI tasks including text generation and code completion.

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 I/O Connect event held in Berlin, which gathered over 1300 developers from more than 100 countries.

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

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 ...