huggingface/hub-docs

Features

• Machine Learning Hubs - A collaborative platform for hosting, versioning, and discovering machine learning models, datasets, and interactive applications using Git.
• Model Hosting Platforms - Hosts and serves pre-trained machine learning models with standardized metadata, documentation, and browser-based inference widgets.
• Web-Based Model Hosting - Hosts machine learning models with documentation and browser-based inference widgets for testing and programmatic access.
• Streaming Dataset Loaders - Enables storing and retrieving large-scale datasets with streaming capabilities for efficient access to data exceeding local memory.
• Machine Learning Datasets - Manages, documents, and streams large-scale datasets with structured metadata for improved discoverability and access.
• Git and Version Control - Provides Git-based repositories to track commit history, branching, and file changes for machine learning models and datasets.
• Dataset Management Frameworks - Manages and versions large-scale datasets with structured metadata, schema definitions, and streaming capabilities.
• Git Large File Storage - Manages massive binary artifacts by offloading large files to external storage while maintaining lightweight pointers in the repository.
• Git-Based Repositories - Uses Git-based repositories to provide version control, branching, and commit history for machine learning artifacts.
• Data Versioning Repositories - Provides Git-based repository hosting for models and datasets with support for version control and large file handling.
• Machine Learning Security - Protecting assets through access tokens, GPG commit signing, malware scanning, and gated access requirements for sensitive datasets and models.
• Repository Access Controls - Enforces authentication, permission checks, and user agreements before granting access to sensitive machine learning assets.
• Machine Learning Demo Platforms - Provides interactive web-based environments for showcasing and testing machine learning models directly in the browser.
• Model Evaluation Metrics - Records standardized performance metrics and evaluation conditions within model cards to provide verifiable benchmarks.
• Model Metadata Loggers - Supports attaching structured metadata to model repositories to enable discovery and interoperability.
• Team Collaboration Management - Organizes teams into organizations to centralize resource sharing, access roles, and billing for machine learning development.
• Metadata-Driven Discovery - Enables programmatic filtering and discovery of machine learning assets through structured metadata embedded in documentation.
• Static Site Generation - Transforms documentation source files into optimized web pages during the build process for consistent content delivery.
• Dataset Metadata Schemas - Enables specifying data types and feature structures to facilitate automated validation and integration of datasets.
• Dataset Metadata Mapping - Allows attaching structured information like licensing and task categories to datasets to improve discoverability.
• Team Management - Supports grouping users into teams to share resources, assign access roles, and manage collective machine learning projects.
• Application Runtime Containers - Executes interactive web demos within isolated container environments to support diverse Python frameworks and hardware acceleration.
• Interactive Demo Deployments - Deploys interactive web-based machine learning demos using Python SDKs, static HTML, or containerized environments.
• Object Storage Providers - Provides S3-compatible object storage for large files like training checkpoints that do not require version control.
• Contact-Gated Access - Requires users to accept terms or provide information before granting access to sensitive datasets.
• Organization and Project Group Roles - Enables grouping users into organizations to centralize project management, role-based access, and shared billing.

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