Hugging Face Hub documentation · Hugging Face Hub documentation Hugging Face Hub documentation Hub 🏡 View all docsAWS Trainium & InferentiaAccelerateArgillaAutoTrainBitsandbytesCLIChat UIDataset viewerDatasetsDeploying on AWSDiffusersDistilabelEvaluateGoogle CloudGoogle TPUsGradioHubHub Python LibraryHuggingface.jsInference Endpoints (dedicated)Inference ProvidersKernelsLeRobotLeaderboardsLightevalMicrosoft AzureOpenEnvOptimumPEFTReachy MiniSafetensorsSentence TransformersTRLTasksText Embeddings InferenceText Generation InferenceTokenizersTrackioTransformersTransformers.jsXetsmolagentstimm Search documentation EN API docs Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes Sign Up to get started Copy page Hugging Face Hub documentation The Hugging Face Hub is the reference AI platform for open ML. It hosts over 2M models, 1.5M datasets, and 1.5M AI apps (Spaces), all open and publicly available. Beyond open AI, the Hub is also a great collaboration platform for internal and private teams. Explore, experiment, collaborate, and build, all in one place! 🤗 Subscriptions & Plans PRO subscription Team & Enterprise Plans Single Sign-On (SSO) Audit Logs Storage Regions Data Studio for Private datasets Resource Groups Advanced Security Tokens Management Network Security Rate Limits Repositories Getting Started Repository Settings Storage Limits Storage Backend (Xet) Local Cache Pull requests and Discussions Notifications Collections Webhooks Next Steps Licenses Models The Model Hub Model Cards Eval Results Gated Models Uploading Models Downloading Models Libraries Tasks Widgets Inference Providers Download Stats Datasets Introduction Datasets Overview Dataset Cards Gated Datasets Uploading Datasets Ingesting Datasets Downloading Datasets Streaming Datasets Editing Datasets Libraries Data Studio Download Stats Data files Configuration Spaces Introduction Spaces Overview Gradio Spaces Static HTML Spaces Docker Spaces ZeroGPU Spaces Embed your Space Run with Docker Reference Advanced Topics Sign in with HF Storage Buckets new Introduction Buckets vs Git Repositories Creating a Bucket Managing Files Use Cases Security & Compliance Jobs Introduction Jobs Overview Quickstart Pricing Manage Jobs Jobs Configuration Popular images Schedule Jobs Webhooks Automation Reference Agents Introduction Agents Overview Hugging Face CLI for AI Agents Hugging Face MCP Server Hugging Face Agent Skills Building agents with the HF SDK Local Agents Agent Libraries Other Organizations Billing Security Moderation Paper Pages Academia Hub Blog Articles Search Digital Object Identifier (DOI) Hub API Endpoints Sign in with HF Contributor Code of Conduct Content Guidelines What’s the Hugging Face Hub? We are helping the community work together towards the goal of advancing Machine Learning 🔥. No single company, including the Tech Titans, will be able to “solve AI” by themselves – the only way we’ll achieve this is by sharing knowledge and resources in a community-centric approach. We are building the largest open-source collection of models, datasets, and demos on the Hugging Face Hub to democratize and advance ML for everyone 🚀. We encourage you to read the Code of Conduct and the Content Guidelines to familiarize yourself with the values that we expect our community members to uphold 🤗. What can you find on the Hub? The Hugging Face Hub hosts Git-based repositories, which are version-controlled folders that can contain all your files. For non-versioned, mutable object storage, the Hub also offers Storage Buckets. On it, you’ll be able to upload and discover… Models: hosting the latest state-of-the-art models for LLM, text, vision, and audio tasks Datasets: featuring a wide variety of data for different domains and modalities Spaces: interactive apps for demonstrating ML models directly in your browser The Hub offers versioning, commit history, diffs, branches, and over a dozen library integrations! All repositories build on Xet, a new technology to efficiently store Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. You can learn more about the features that all repositories share in the Repositories documentation. Models You can discover and use dozens of thousands of open-source ML models shared by the community. To promote responsible model usage and development, model repos are equipped with Model Cards to inform users of each model’s limitations and biases. Additional metadata about info such as their tasks, languages, and evaluation results can be included, with training metrics charts even added if the repository contains TensorBoard traces. It’s also easy to add an inference widget to your model, allowing anyone to play with the model directly in the browser! For programmatic access, a serverless API is provided by Inference Providers. To upload models to the Hub, or download models and integrate them into your work, explore the Models documentation. You can also choose from over a dozen libraries such as 🤗 Transformers, Asteroid, and ESPnet that support the Hub. Datasets The Hub is home to over 500k public datasets in more than 8k languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio. The Hub makes it simple to find, download, and upload datasets. Datasets are accompanied by extensive documentation in the form of Dataset Cards and Data Studio to let you explore the data directly in your browser. While many datasets are public, organizations and individuals can create private datasets to comply with licensing or privacy issues. You can learn more about Datasets here on the Hugging Face Hub documentation. The 🤗 datasets library allows you to programmatically interact with the datasets, so you can easily use datasets from the Hub in your projects. With a single line of code, you can access the datasets; even if they are so large they don’t fit in your computer, you can use streaming to efficiently access the data. Spaces Spaces is a simple way to host ML demo apps on the Hub. They allow you to build your ML portfolio, showcase your projects at conferences or to stakeholders, and work collaboratively with other people in the ML ecosystem. We currently support two awesome Python SDKs (Gradio and Streamlit) that let you build cool apps in a matter of minutes. Users can also create static Spaces, which are simple HTML/CSS/JavaScript pages, or deploy any Docker-based application. If you need GPU power for your demos, try ZeroGPU: it dynamically provides NVIDIA RTX Pro 6000 Blackwell GPUs, in real-time, only when needed. After you’ve explored a few Spaces (take a look at our Space of the Week!), dive into the Spaces documentation to learn all about how you can create your own Space. You’ll also be able to upgrade your Space to run on a GPU or other accelerated hardware. ⚡️ Storage Buckets Storage Buckets provide S3-like object storage on Hugging Face, powered by the Xet storage backend. Unlike repositories (which are git-based and track file history), buckets are remote object storage containers designed for large-scale files with content-addressable deduplication. They are designed for use cases where you need simple, fast, mutable storage such as storing training checkpoints, logs, intermediate artifacts, or any large collection of files that doesn’t need version control. Organizations Companies, universities and non-profits are an essential part of the Hugging Face community! The Hub offers Organizations, which can be used to group accounts and manage datasets, models, and Spaces. Educators can also create collaborative organizations for students using Hugging Face for Classrooms. An organization’s repositories will be featured on the organization’s page and every member of the organization will have the ability to contribute to the repository. In addition to conveniently grouping all of an organization’s work, the Hub allows admins to set roles to control access to repositories, and manage their organization’s payment method and billing info. Machine Learning is more fun when collaborating! 🔥 Explore existing organizations, create a new organization here, and then visit the Organizations documentation to learn more. Security The Hugging Face Hub supports security and access control features to give you the peace of mind that your code, models, and data are safe. Visit the Security section in these docs to learn about: User Access Tokens Access Control for Organizations Signing commits with GPG Malware scanning Update on GitHub Team & Enterprise Plans→