Quick start

The Hugging Face Hub is the go-to place for sharing machine learning models, demos, datasets, and metrics. huggingface_hub library helps you interact with the Hub without leaving your development environment. You can create and manage repositories easily, download and upload files, and get useful model and dataset metadata from the Hub.

Install the Hub library

To get started, you should install the huggingface_hub library. You can install the library with pip or conda.

With pip:

pip install huggingface_hub

With conda:

conda install -c conda-forge huggingface_hub

Download files from the Hub

Repositories on the Hub are git version controlled, and users can download a single file or the whole repository. You can use the hf_hub_download() function to download files. This function will download and cache a file on your local disk. The next time you need that file, it will load from your cache, so you don’t need to re-download it.

You will need the repository id and the filename of the file you want to download. For example, to download the Pegasus model configuration file:

>>> from huggingface_hub import hf_hub_download
>>> hf_hub_download(repo_id="google/pegasus-xsum", filename="config.json")

To download a specific version of the file, use the revision parameter to specify the branch name, tag, or commit hash. If you choose to use the commit hash, it must be the full-length hash instead of the shorter 7-character commit hash:

>>> from huggingface_hub import hf_hub_download
>>> hf_hub_download(
...     repo_id="google/pegasus-xsum", 
...     filename="config.json", 
...     revision="4d33b01d79672f27f001f6abade33f22d993b151"
... )

For more details and options, see the API reference for hf_hub_download().

Login

In a lot of cases, you must be logged in with a Hugging Face account to interact with the Hub: download private repos, upload files, create PRs,… Create an account if you don’t already have one, and then sign in to get your User Access Token from your Settings page. The User Access Token is used to authenticate your identity to the Hub.

Once you have your User Access Token, run the following command in your terminal:

huggingface-cli login

Or if you prefer to work from a Jupyter or Colaboratory notebook, then login with notebook_login():

>>> from huggingface_hub import notebook_login
>>> notebook_login()

Create a repository

Once you’ve registered and logged in, create a repository with the create_repo() function:

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.create_repo(repo_id="super-cool-model")

If you want your repository to be private, then:

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.create_repo(repo_id="super-cool-model", private=True)

Private repositories will not be visible to anyone except yourself.

Share files to the Hub

Use the upload_file() function to add a file to your newly created repository. You need to specify:

1. The path of the file to upload.
2. The path of the file in the repository.
3. The repository id of where you want to add the file.

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.upload_file(path_or_fileobj="/home/lysandre/dummy-test/README.md", 
...                 path_in_repo="README.md", 
...                 repo_id="lysandre/test-model",
... )

To upload more than one file at a time, take a look at this guide which will introduce you to several methods for uploading files (with or without git).

Next steps

The huggingface_hub library provides an easy way for users to interact with the Hub with Python. To learn more about how you can manage your files and repositories on the Hub, we recommend reading our how-to guides for how to:

• Create and manage repositories.
• Download files from the Hub.
• Upload files to the Hub.
• Search the Hub for your desired model or dataset.
• Access the Inference API for fast inference.