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Host Git-based models, datasets and Spaces on the Hugging Face Hub.

State-of-the-art ML for Pytorch, TensorFlow, and JAX.

State-of-the-art diffusion models for image and audio generation in PyTorch.

Access and share datasets for computer vision, audio, and NLP tasks.

Build machine learning demos and other web apps, in just a few lines of Python.

Hub Python Library

Client library for the HF Hub: manage repositories from your Python runtime.

A collection of JS libraries to interact with Hugging Face, with TS types included.

Use more than 50k models through our public inference API, with scalability built-in.

Inference Endpoints

Easily deploy your model to production on dedicated, fully managed infrastructure.

Easily train and use PyTorch models with multi-GPU, TPU, mixed-precision.

Fast training and inference of HF Transformers with easy to use hardware optimization tools.

Fast tokenizers, optimized for both research and production.

This course will teach you about natural language processing using libraries from the HF ecosystem.

This course will teach you about deep reinforcement learning using libraries from the HF ecosystem.

Evaluate and report model performance easier and more standardized.

All things about ML tasks: demos, use cases, models, datasets, and more!

Datasets-server

API to access the contents, metadata and basic statistics of all Hugging Face Hub datasets.

Create and share simulation environments for intelligent agents and synthetic data generation.

Amazon SageMaker

Train and Deploy Transformer models with Amazon SageMaker and Hugging Face DLCs.

State-of-the-art computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts.

Simple, safe way to store and distribute tensors.

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