The official CLI and Python client for the Hugging Face Hub.
About
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Documentation
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Install
·
CLI Guide
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Contributing
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Quick start
Install the hf CLI with the standalone installer:
# On macOS and Linux.
curl -LsSf https://hf.co/cli/install.sh | bash
# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"
Log in, then start working with the Hub:
# Log in (use --token $HF_TOKEN in non-interactive environments)
hf auth login
# Find models served by Inference Providers
hf models ls --warm
# Download a model
hf download Qwen/Qwen3-0.6B
# Upload files to your own repo
hf upload username/my-cool-model ./model.safetensors
# Sync a local folder to a storage bucket
hf buckets sync ./checkpoints hf://buckets/username/my-bucket
# Run a job on Hugging Face infrastructure
hf jobs run python:3.12 python -c "print('Hello from the cloud!')"
# Discover everything else
hf --help
The Hub uses tokens to authenticate applications (see docs). Check out the CLI guide for a tour of the main features.
What is huggingface_hub?
The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects, play with the thousands of machine learning apps hosted on the Hub, or create and share your own models, datasets and demos with the community. Everything ships in one package with two interfaces: the hf CLI for your terminal and the huggingface_hub library for Python — both designed to work well for humans and AI agents. Use them to:
- Download files from the Hub.
- Upload files to the Hub.
- Manage your repositories.
- Run Inference on deployed models.
- Run Jobs on Hugging Face infrastructure.
- Search for models, datasets and Spaces.
- Share Model Cards to document your models.
- Engage with the community through PRs and comments.
- Do all of the above from the terminal with the
hfCLI.
Built for humans and AI agents
The hf CLI is designed for people and coding agents alike: the same commands adapt their output when run by an agent. If you use Claude Code, Codex, Cursor, or another coding agent, install the hf CLI Skill — a command reference generated from your installed CLI:
# works with Claude Code, Codex, Cursor, OpenCode, Pi and any agent that loads skills from `.agents/skills`
hf skills add
Learn more in the Hugging Face CLI for AI agents guide and the announcement blog post.
Use the Python library
Install the huggingface_hub package with pip (this also installs the hf CLI):
pip install huggingface_hub
We recommend using uv for a fast and reliable install:
uv pip install huggingface_hub
In order to keep the package minimal by default, huggingface_hub comes with optional dependencies useful for some use cases. For example, if you want to use the MCP module, run:
pip install "huggingface_hub[mcp]"
To learn more about installation and optional dependencies, check out the installation guide.
Download files
Download a single file
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="zai-org/GLM-5.2", filename="config.json")
Or an entire repository
from huggingface_hub import snapshot_download
snapshot_download("sentence-transformers/all-MiniLM-L6-v2")
Files will be downloaded in a local cache folder. More details in this guide.
Create a repository
from huggingface_hub import create_repo
create_repo(repo_id="super-cool-model")
Upload files
Upload a single file
from huggingface_hub import upload_file
upload_file(
path_or_fileobj="/home/lysandre/dummy-test/README.md",
path_in_repo="README.md",
repo_id="lysandre/test-model",
)
Or an entire folder
from huggingface_hub import upload_folder
upload_folder(
folder_path="/path/to/local/space",
repo_id="username/my-cool-space",
repo_type="space",
)
More details in the upload guide.
Integrating with the Hub.
We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations here.
The advantages are:
- Free model or dataset hosting for libraries and their users.
- Built-in file versioning, even with very large files, made possible by Xet, the Hub's chunk-deduplicated storage backend.
- In-browser widgets to play with the uploaded models.
- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
- Usage stats and more features to come.
If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a step-by-step guide with ❤️ showing how to do this integration.
Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community. We wrote a contribution guide to summarize how to get started to contribute to this repository.
Release files for huggingface-hub 2.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| huggingface_hub-2.0.0.tar.gz | 1.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| huggingface_hub-2.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.9 MB
Release files / huggingface_hub-2.0.0.tar.gz
| Download URL | huggingface_hub-2.0.0.tar.gz |
|---|---|
| Size | 1.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency logRelease files / huggingface_hub-2.0.0-py3-none-any.whl
| Download URL | huggingface_hub-2.0.0-py3-none-any.whl |
|---|---|
| Size | 828.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b3eecb60540c17e8ef94c187104f4a5c0c451afcb9510ba8e17739bedcc2d6dd
|
|
BLAKE2b-256 checksum How to use checksums |
5ff9d752d5756ce0405ba29405b65517b287d2baf9f77a1cf5329266f3d577f0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
Transparency log
