google (Google)

Welcome to the official Google organization on Hugging Face!

Featured Models and Tools

Gemma Family of Open Multimodal Models
- Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models
- PaliGemma is a versatile and lightweight vision-language model (VLM)
- CodeGemma is a collection of lightweight open code models built on top of Gemma
- RecurrentGemma is a family of open language models built on a novel recurrent architecture developed at Google
- ShieldGemma is a series of safety content moderation models built upon Gemma 2 that target four harm categories
Health AI Developer Foundations
- CXR Foundation embedding model for efficiently building AI for chest X-ray applications
- Path Foundation embedding model for efficiently building AI for histopathology applications
- Derm Foundation embedding model for efficiently building AI for skin imaging applications
BERT, T5, and TimesFM Model Families
Author ML models with MaxText, JAX, Keras, Tensorflow, and PyTorch/XLA
SynthID is a Google DeepMind technology that watermarks and identifies AI-generated content (🤗 Space)

Google Blogs:
Notable GitHub Repositories:
- https://github.com/google/jax is a Python library for high-performance numerical computing and machine learning
- https://github.com/huggingface/Google-Cloud-Containers facilitate the training and deployment of Hugging Face models on Google Cloud
- https://github.com/pytorch/xla enables PyTorch on XLA Devices (e.g. Google TPU)
- https://github.com/huggingface/optimum-tpu brings the power of TPUs to your training and inference stack
- https://github.com/openxla/xla is a machine learning compiler for GPUs, CPUs, and ML accelerators
- https://github.com/google/JetStream (and https://github.com/google/jetstream-pytorch) is a throughput and memory optimized engine for large language model (LLM) inference on XLA devices
- https://github.com/google/flax is a neural network library for JAX that is designed for flexibility
- https://github.com/kubernetes-sigs/lws facilitates Kubernetes deployment patterns for AI/ML inference workloads, especially multi-host inference workloads
- https://github.com/GoogleCloudPlatform/ai-on-gke is a collection of AI examples, best-practices, and prebuilt solutions
Google AI Research Papers: https://research.google/

Customize and run common ML Tasks with low-code MediaPipe Solutions
Run pretrained or custom models on-device with Lite RT (previously known as TensorFlow Lite)
Convert TensorFlow and JAX models to LiteRT
Convert PyTorch models to LiteRT and author high performance on-device LLMs with AI Edge Torch
Visualize and debug models with Model Explorer (🤗 Space)

Select Google Cloud CPU, GPU, or TPU options when setting up your Hugging Face Inference Endpoints and Spaces
Train and Deploy Hugging Face models on Google Kubernetes Engine (GKE) and Vertex AI directly from Hugging Face model landing pages or from Google Cloud Model Garden
Integrate Colab notebooks with Hugging Face Hub via the HF_TOKEN secret manager integration and transformers/huggingface_hub pre-installs
Leverage Hugging Face Deep Learning Containers (DLCs) for easy training and deployment of Hugging Face models on Google Cloud infrastructure
Run optimized, zero-configuration inference microservices with Hugging Face Generative AI Services (HUGS) via the Google Cloud Marketplace

Read about our principles for responsible AI at https://ai.google/responsibility/principles