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Provider Summary
manage model repositories
upload and download files
run model inference
search models and datasets
create and manage Spaces
manage discussions and pull requests
host and browse datasets
curate collections
manage organizations
configure webhooks
Manage machine learning model, dataset, and Spaces repositories on Hugging Face Hub. Create, update, delete, and configure Git-based repositories with version control, branching, tagging, and pull requests. Upload and download model files and datasets. Run model inference for tasks like text generation, image generation, summarization, embeddings, and classification via serverless or dedicated endpoints. Search and discover models, datasets, and Spaces by author, task, language, or tags. Create and manage Spaces for hosting ML-powered application demos. Participate in community discussions, open pull requests, and post comments on repositories. Organize related items into collections. Manage organizations and access control. Listen for repository, discussion, and comment events via webhooks.
Get information about the authenticated user, including username, email, organizations, and account details.
Run a chat completion using a model on the Hugging Face Inference API. Follows the OpenAI-compatible chat completions format. Supports conversation history with system, user, and assistant messages.
Retrieve a Hugging Face collection by its slug. Returns the collection's title, description, and all items (models, datasets, spaces) it contains.
List discussions and pull requests on a Hugging Face repository. Returns summaries including title, status, and whether each item is a PR.
List files and directories in a Hugging Face repository at a given path and revision. Returns file metadata including type, size, and OID.
Create a new model, dataset, or Space repository on Hugging Face Hub. Supports setting visibility, SDK type (for Spaces), and license.
Get runtime information for a Space, including hardware, stage, SDK, and storage details.
Search for datasets on Hugging Face Hub. Filter by keyword, author, and tags. Results include dataset metadata such as downloads and likes.
Search for machine learning models on Hugging Face Hub. Filter by keyword, author, library framework, pipeline task, and tags. Results include model metadata such as downloads, likes, and pipeline task.
Search for Spaces (ML application demos) on Hugging Face Hub. Filter by keyword, author, and tags. Results include Space metadata such as SDK type and likes.
This integration is licensed under the AGPL-3.0 License.
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