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| title: README | |
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| *Welcome to the official Google organization on Hugging Face\!* | |
| [Google collaborates with Hugging Face](https://huggingface.co/blog/gcp-partnership) across open science, open source, cloud, and hardware to **enable companies to innovate with AI** [on Google Cloud AI services and infrastructure with the Hugging Face ecosystem](https://huggingface.co/docs/google-cloud/main/en/index). | |
| ## 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**](https://huggingface.co/collections/google/health-ai-developer-foundations-hai-def-6744dc060bc19b6cf631bb0f) | |
| * [**MedGemma**](https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4) collection of open models for medical image and text comprehension to accelerate building healthcare-based AI applications | |
| * [**TxGemma**](https://huggingface.co/collections/google/txgemma-release-67dd92e931c857d15e4d1e87) collection of open models to accelerate the development of therapeutics | |
| * [**CXR Foundation**](https://huggingface.co/google/cxr-foundation) embedding model for efficiently building AI for chest X-ray applications | |
| * [**Path Foundation**](https://huggingface.co/google/path-foundation) embedding model for efficiently building AI for histopathology applications | |
| * [**Derm Foundation**](https://huggingface.co/google/derm-foundation) embedding model for efficiently building AI for skin imaging applications | |
| * **HeAR** ([TensorFlow](https://huggingface.co/google/hear), [PyTorch](https://huggingface.co/google/hear-pytorch)) embedding model for efficiently building AI related to audio originating from the respiratory system | |
| * **[**BERT**](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc), [**T5**](https://huggingface.co/collections/google/t5-release-65005e7c520f8d7b4d037918), and [**TimesFM**](https://github.com/google-research/timesfm) Model Families** | |
| * **Author ML models with [**MaxText**](https://github.com/google/maxtext), [**JAX**](https://github.com/google/jax), [**Keras**](https://github.com/keras-team/keras), [**Tensorflow**](https://github.com/tensorflow/tensorflow), and [**PyTorch/XLA**](https://github.com/pytorch/xla)** | |
| * **[**SynthID**](https://deepmind.google/technologies/synthid/)** is a Google DeepMind technology that watermarks and identifies AI-generated content ([🤗 Space](https://huggingface.co/spaces/google/synthid-text)) | |
| ## Open Research and Community Resources | |
| * **Google Blogs**: | |
| * [https://blog.google/](https://blog.google/) | |
| * [https://cloud.google.com/blog/](https://cloud.google.com/blog/) | |
| * [https://deepmind.google/discover/blog/](https://deepmind.google/discover/blog/) | |
| * [https://developers.google.com/learn?category=aiandmachinelearning](https://developers.google.com/learn?category=aiandmachinelearning) | |
| * [https://research.google/blog/](https://research.google/blog/) | |
| * **Notable GitHub Repositories**: | |
| * [https://github.com/google/jax](https://github.com/google/jax) is a Python library for high-performance numerical computing and machine learning | |
| * [https://github.com/huggingface/Google-Cloud-Containers](https://github.com/huggingface/Google-Cloud-Containers) facilitate the training and deployment of Hugging Face models on Google Cloud | |
| * [https://github.com/pytorch/xla](https://github.com/pytorch/xla) enables PyTorch on XLA Devices (e.g. Google TPU) | |
| * [https://github.com/huggingface/optimum-tpu](https://github.com/huggingface/optimum-tpu) brings the power of TPUs to your training and inference stack | |
| * [https://github.com/openxla/xla](https://github.com/openxla/xla) is a machine learning compiler for GPUs, CPUs, and ML accelerators | |
| * [https://github.com/google/JetStream](https://github.com/google/JetStream) (and [https://github.com/google/jetstream-pytorch](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](https://github.com/google/flax) is a neural network library for JAX that is designed for flexibility | |
| * [https://github.com/kubernetes-sigs/lws](https://github.com/kubernetes-sigs/lws) facilitates Kubernetes deployment patterns for AI/ML inference workloads, especially multi-host inference workloads | |
| * [https://gke-ai-labs.dev/](https://gke-ai-labs.dev/) is a collection of AI examples, best-practices, and prebuilt solutions | |
| * **Google Research Papers**: [https://research.google/](https://research.google/) | |
| ## On-device ML using [Google AI Edge](http://ai.google.dev/edge) | |
| * Customize and run common ML Tasks with low-code [MediaPipe Solutions](https://ai.google.dev/edge/mediapipe/solutions/guide) | |
| * Run [pretrained](https://ai.google.dev/edge/litert/models/trained) or custom models on-device with [Lite RT (previously known as TensorFlow Lite)](https://ai.google.dev/edge/lite) | |
| * Convert [TensorFlow](https://ai.google.dev/edge/lite/models/convert_tf) and [JAX](https://ai.google.dev/edge/lite/models/convert_jax) models to LiteRT | |
| * Convert PyTorch models to LiteRT and author high performance on-device LLMs with [AI Edge Torch](https://github.com/google-ai-edge/ai-edge-torch) | |
| * Visualize and debug models with [Model Explorer](https://ai.google.dev/edge/model-explorer) ([🤗 Space](https://huggingface.co/spaces/google/model-explorer)) | |
| ## Partnership Highlights and Resources | |
| * Select Google Cloud CPU, GPU, or TPU options when setting up your **Hugging Face [**Inference Endpoints**](https://huggingface.co/blog/tpu-inference-endpoints-spaces) 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**](https://colab.research.google.com/) notebooks with Hugging Face Hub** via the [HF\_TOKEN secret manager integration](https://huggingface.co/docs/huggingface_hub/v0.23.3/en/quick-start#environment-variable) and transformers/huggingface\_hub pre-installs | |
| * Leverage [**Hugging Face Deep Learning Containers (DLCs)**](https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face) 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**](https://huggingface.co/docs/hugs/how-to/cloud/gcp) | |
| Read about our principles for responsible AI at [https://ai.google/responsibility/principles](https://ai.google/responsibility/principles/) |