Hub Python Library documentation

Command Line Interface (CLI)

Hub Python Library

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Command Line Interface (CLI)

The huggingface_hub Python package comes with a built-in CLI called huggingface-cli. This tool allows you to interact with the Hugging Face Hub directly from a terminal. For example, you can login to your account, create a repository, upload and download files, etc. It also comes with handy features to configure your machine or manage your cache. In this guide, we will have a look at the main features of the CLI and how to use them.

Getting started

First of all, let’s install the CLI:

>>> pip install -U "huggingface_hub[cli]"

In the snippet above, we also installed the [cli] extra dependencies to make the user experience better, especially when using the delete-cache command.

Once installed, you can check that the CLI is correctly setup:

>>> huggingface-cli --help
usage: huggingface-cli <command> [<args>]

positional arguments:
  {env,login,whoami,logout,repo,upload,download,lfs-enable-largefiles,lfs-multipart-upload,scan-cache,delete-cache}
                        huggingface-cli command helpers
    env                 Print information about the environment.
    login               Log in using a token from huggingface.co/settings/tokens
    whoami              Find out which huggingface.co account you are logged in as.
    logout              Log out
    repo                {create} Commands to interact with your huggingface.co repos.
    upload              Upload a file or a folder to a repo on the Hub
    download            Download files from the Hub
    lfs-enable-largefiles
                        Configure your repository to enable upload of files > 5GB.
    scan-cache          Scan cache directory.
    delete-cache        Delete revisions from the cache directory.

options:
  -h, --help            show this help message and exit

If the CLI is correctly installed, you should see a list of all the options available in the CLI. If you get an error message such as command not found: huggingface-cli, please refer to the Installation guide.

The --help option is very convenient for getting more details about a command. You can use it anytime to list all available options and their details. For example, huggingface-cli upload --help provides more information on how to upload files using the CLI.

huggingface-cli login

In many cases, you must be logged in to a Hugging Face account to interact with the Hub (download private repos, upload files, create PRs, etc.). To do so, you need a User Access Token from your Settings page. The User Access Token is used to authenticate your identity to the Hub. Make sure to set a token with write access if you want to upload or modify content.

Once you have your token, run the following command in your terminal:

>>> huggingface-cli login

This command will prompt you for a token. Copy-paste yours and press Enter. Then you’ll be asked if the token should also be saved as a git credential. Press Enter again (default to yes) if you plan to use git locally. Finally, it will call the Hub to check that your token is valid and save it locally.

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_|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|

To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .
Token: 
Add token as git credential? (Y/n) 
Token is valid (permission: write).
Your token has been saved in your configured git credential helpers (store).
Your token has been saved to /home/wauplin/.cache/huggingface/token
Login successful

Alternatively, if you want to log-in without being prompted, you can pass the token directly from the command line. To be more secure, we recommend passing your token as an environment variable to avoid pasting it in your command history.

# Or using an environment variable
>>> huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential 
Token is valid (permission: write).
Your token has been saved in your configured git credential helpers (store).
Your token has been saved to /home/wauplin/.cache/huggingface/token
Login successful

huggingface-cli whoami

If you want to know if you are logged in, you can use huggingface-cli whoami. This command doesn’t have any options and simply prints your username and the organizations you are a part of on the Hub:

huggingface-cli whoami                                                                     
Wauplin
orgs:  huggingface,eu-test,OAuthTesters,hf-accelerate,HFSmolCluster

If you are not logged in, an error message will be printed.

huggingface-cli logout

This commands logs you out. In practice, it will delete the token saved on your machine.

This command will not log you out if you are logged in using the HF_TOKEN environment variable (see reference). If that is the case, you must unset the environment variable in your machine configuration.

huggingface-cli download

Use the huggingface-cli download command to download files from the Hub directly. Internally, it uses the same hf_hub_download() and snapshot_download() helpers described in the Download guide and prints the returned path to the terminal. In the examples below, we will walk through the most common use cases. For a full list of available options, you can run:

huggingface-cli download --help

Download a single file

To download a single file from a repo, simply provide the repo_id and filename as follow:

>>> huggingface-cli download gpt2 config.json
downloading https://huggingface.co/gpt2/resolve/main/config.json to /home/wauplin/.cache/huggingface/hub/tmpwrq8dm5o
(…)ingface.co/gpt2/resolve/main/config.json: 100%|██████████████████████████████████| 665/665 [00:00<00:00, 2.49MB/s]
/home/wauplin/.cache/huggingface/hub/models--gpt2/snapshots/11c5a3d5811f50298f278a704980280950aedb10/config.json

The command will always print on the last line the path to the file on your local machine.

Download an entire repository

In some cases, you just want to download all the files from a repository. This can be done by just specifying the repo id:

>>> huggingface-cli download HuggingFaceH4/zephyr-7b-beta
Fetching 23 files:   0%|                                                | 0/23 [00:00<?, ?it/s]
...
...
/home/wauplin/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/3bac358730f8806e5c3dc7c7e19eb36e045bf720

Download multiple files

You can also download a subset of the files from a repository with a single command. This can be done in two ways. If you already have a precise list of the files you want to download, you can simply provide them sequentially:

>>> huggingface-cli download gpt2 config.json model.safetensors
Fetching 2 files:   0%|                                                                        | 0/2 [00:00<?, ?it/s]
downloading https://huggingface.co/gpt2/resolve/11c5a3d5811f50298f278a704980280950aedb10/model.safetensors to /home/wauplin/.cache/huggingface/hub/tmpdachpl3o
(…)8f278a7049802950aedb10/model.safetensors: 100%|██████████████████████████████| 8.09k/8.09k [00:00<00:00, 40.5MB/s]
Fetching 2 files: 100%|████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00,  3.76it/s]
/home/wauplin/.cache/huggingface/hub/models--gpt2/snapshots/11c5a3d5811f50298f278a704980280950aedb10

The other approach is to provide patterns to filter which files you want to download using --include and --exclude. For example, if you want to download all safetensors files from stabilityai/stable-diffusion-xl-base-1.0, except the files in FP16 precision:

>>> huggingface-cli download stabilityai/stable-diffusion-xl-base-1.0 --include "*.safetensors" --exclude "*.fp16.*"*
Fetching 8 files:   0%|                                                                         | 0/8 [00:00<?, ?it/s]
...
...
Fetching 8 files: 100%|█████████████████████████████████████████████████████████████████████████| 8/8 (...)
/home/wauplin/.cache/huggingface/hub/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots/462165984030d82259a11f4367a4eed129e94a7b

Download a dataset or a Space

The examples above show how to download from a model repository. To download a dataset or a Space, use the --repo-type option:

# https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k
>>> huggingface-cli download HuggingFaceH4/ultrachat_200k --repo-type dataset

# https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
>>> huggingface-cli download HuggingFaceH4/zephyr-chat --repo-type space

...

Download a specific revision

The examples above show how to download from the latest commit on the main branch. To download from a specific revision (commit hash, branch name or tag), use the --revision option:

>>> huggingface-cli download bigcode/the-stack --repo-type dataset --revision v1.1
...

Download to a local folder

The recommended (and default) way to download files from the Hub is to use the cache-system. However, in some cases you want to download files and move them to a specific folder. This is useful to get a workflow closer to what git commands offer. You can do that using the --local_dir option.

Downloading to a local directory comes with some downsides. Please check out the limitations in the Download guide before using --local-dir.

>>> huggingface-cli download adept/fuyu-8b model-00001-of-00002.safetensors --local-dir .
...
./model-00001-of-00002.safetensors

Specify cache directory

By default, all files will be download to the cache directory defined by the HF_HOME environment variable. You can also specify a custom cache using --cache-dir:

>>> huggingface-cli download adept/fuyu-8b --cache-dir ./path/to/cache
...
./path/to/cache/models--adept--fuyu-8b/snapshots/ddcacbcf5fdf9cc59ff01f6be6d6662624d9c745

Specify a token

To access private or gated repositories, you must use a token. By default, the token saved locally (using huggingface-cli login) will be used. If you want to authenticate explicitly, use the --token option:

>>> huggingface-cli download gpt2 config.json --token=hf_****
/home/wauplin/.cache/huggingface/hub/models--gpt2/snapshots/11c5a3d5811f50298f278a704980280950aedb10/config.json

Quiet mode

By default, the huggingface-cli download command will be verbose. It will print details such as warning messages, information about the downloaded files, and progress bars. If you want to silence all of this, use the --quiet option. Only the last line (i.e. the path to the downloaded files) is printed. This can prove useful if you want to pass the output to another command in a script.

>>> huggingface-cli download gpt2 --quiet
/home/wauplin/.cache/huggingface/hub/models--gpt2/snapshots/11c5a3d5811f50298f278a704980280950aedb10

huggingface-cli upload

Use the huggingface-cli upload command to upload files to the Hub directly. Internally, it uses the same upload_file() and upload_folder() helpers described in the Upload guide. In the examples below, we will walk through the most common use cases. For a full list of available options, you can run:

>>> huggingface-cli upload --help

Upload an entire folder

The default usage for this command is:

# Usage:  huggingface-cli upload [repo_id] [local_path] [path_in_repo]

To upload the current directory at the root of the repo, use:

>>> huggingface-cli upload my-cool-model . .
https://huggingface.co/Wauplin/my-cool-model/tree/main/

If the repo doesn’t exist yet, it will be created automatically.

You can also upload a specific folder:

>>> huggingface-cli upload my-cool-model ./models .
https://huggingface.co/Wauplin/my-cool-model/tree/main/

Finally, you can upload a folder to a specific destination on the repo:

>>> huggingface-cli upload my-cool-model ./path/to/curated/data /data/train
https://huggingface.co/Wauplin/my-cool-model/tree/main/data/train

Upload a single file

You can also upload a single file by setting local_path to point to a file on your machine. If that’s the case, path_in_repo is optional and will default to the name of your local file:

>>> huggingface-cli upload Wauplin/my-cool-model ./models/model.safetensors
https://huggingface.co/Wauplin/my-cool-model/blob/main/model.safetensors

If you want to upload a single file to a specific directory, set path_in_repo accordingly:

>>> huggingface-cli upload Wauplin/my-cool-model ./models/model.safetensors /vae/model.safetensors
https://huggingface.co/Wauplin/my-cool-model/blob/main/vae/model.safetensors

Upload multiple files

To upload multiple files from a folder at once without uploading the entire folder, use the --include and --exclude patterns. It can also be combined with the --delete option to delete files on the repo while uploading new ones. In the example below, we sync the local Space by deleting remote files and uploading all files except the ones in /logs:

# Sync local Space with Hub (upload new files except from logs/, delete removed files)
>>> huggingface-cli upload Wauplin/space-example --repo-type=space --exclude="/logs/*" --delete="*" --commit-message="Sync local Space with Hub"
...

Upload to a dataset or Space

To upload to a dataset or a Space, use the --repo-type option:

>>> huggingface-cli upload Wauplin/my-cool-dataset ./data /train --repo-type=dataset
...

Upload to an organization

To upload content to a repo owned by an organization instead of a personal repo, you must explicitly specify it in the repo_id:

>>> huggingface-cli upload MyCoolOrganization/my-cool-model . .
https://huggingface.co/MyCoolOrganization/my-cool-model/tree/main/

Upload to a specific revision

By default, files are uploaded to the main branch. If you want to upload files to another branch or reference, use the --revision option:

# Upload files to a PR
>>> huggingface-cli upload bigcode/the-stack . . --repo-type dataset --revision refs/pr/104
...

Upload and create a PR

If you don’t have the permission to push to a repo, you must open a PR and let the authors know about the changes you want to make. This can be done by setting the --create-pr option:

# Create a PR and upload the files to it
>>> huggingface-cli upload bigcode/the-stack . . --repo-type dataset --revision refs/pr/104
https://huggingface.co/datasets/bigcode/the-stack/blob/refs%2Fpr%2F104/

Upload at regular intervals

In some cases, you might want to push regular updates to a repo. For example, this is useful if you’re training a model and you want to upload the logs folder every 10 minutes. You can do this using the --every option:

# Upload new logs every 10 minutes
huggingface-cli upload training-model logs/ --every=10

Specify a commit message

Use the --commit-message and --commit-description to set a custom message and description for your commit instead of the default one

>>> huggingface-cli upload Wauplin/my-cool-model ./models . --commit-message="Epoch 34/50" --commit-description="Val accuracy: 68%. Check tensorboard for more details."
...
https://huggingface.co/Wauplin/my-cool-model/tree/main

Specify a token

To upload files, you must use a token. By default, the token saved locally (using huggingface-cli login) will be used. If you want to authenticate explicitly, use the --token option:

>>> huggingface-cli upload Wauplin/my-cool-model ./models . --token=hf_****
...
https://huggingface.co/Wauplin/my-cool-model/tree/main

Quiet mode

By default, the huggingface-cli upload command will be verbose. It will print details such as warning messages, information about the uploaded files, and progress bars. If you want to silence all of this, use the --quiet option. Only the last line (i.e. the URL to the uploaded files) is printed. This can prove useful if you want to pass the output to another command in a script.

>>> huggingface-cli upload Wauplin/my-cool-model ./models . --quiet
https://huggingface.co/Wauplin/my-cool-model/tree/main

huggingface-cli scan-cache

Scanning your cache directory is useful if you want to know which repos you have downloaded and how much space it takes on your disk. You can do that by running huggingface-cli scan-cache:

>>> huggingface-cli scan-cache
REPO ID                     REPO TYPE SIZE ON DISK NB FILES LAST_ACCESSED LAST_MODIFIED REFS                LOCAL PATH
--------------------------- --------- ------------ -------- ------------- ------------- ------------------- -------------------------------------------------------------------------
glue                        dataset         116.3K       15 4 days ago    4 days ago    2.4.0, main, 1.17.0 /home/wauplin/.cache/huggingface/hub/datasets--glue
google/fleurs               dataset          64.9M        6 1 week ago    1 week ago    refs/pr/1, main     /home/wauplin/.cache/huggingface/hub/datasets--google--fleurs
Jean-Baptiste/camembert-ner model           441.0M        7 2 weeks ago   16 hours ago  main                /home/wauplin/.cache/huggingface/hub/models--Jean-Baptiste--camembert-ner
bert-base-cased             model             1.9G       13 1 week ago    2 years ago                       /home/wauplin/.cache/huggingface/hub/models--bert-base-cased
t5-base                     model            10.1K        3 3 months ago  3 months ago  main                /home/wauplin/.cache/huggingface/hub/models--t5-base
t5-small                    model           970.7M       11 3 days ago    3 days ago    refs/pr/1, main     /home/wauplin/.cache/huggingface/hub/models--t5-small

Done in 0.0s. Scanned 6 repo(s) for a total of 3.4G.
Got 1 warning(s) while scanning. Use -vvv to print details.

For more details about how to scan your cache directory, please refer to the Manage your cache guide.

huggingface-cli delete-cache

huggingface-cli delete-cache is a tool that helps you delete parts of your cache that you don’t use anymore. This is useful for saving and freeing disk space. To learn more about using this command, please refer to the Manage your cache guide.

huggingface-cli env

The huggingface-cli env command prints details about your machine setup. This is useful when you open an issue on GitHub to help the maintainers investigate your problem.

>>> huggingface-cli env

Copy-and-paste the text below in your GitHub issue.

- huggingface_hub version: 0.19.0.dev0
- Platform: Linux-6.2.0-36-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/wauplin/.cache/huggingface/token
- Has saved token ?: True
- Who am I ?: Wauplin
- Configured git credential helpers: store
- FastAI: N/A
- Tensorflow: 2.11.0
- Torch: 1.12.1
- Jinja2: 3.1.2
- Graphviz: 0.20.1
- Pydot: 1.4.2
- Pillow: 9.2.0
- hf_transfer: 0.1.3
- gradio: 4.0.2
- tensorboard: 2.6
- numpy: 1.23.2
- pydantic: 2.4.2
- aiohttp: 3.8.4
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /home/wauplin/.cache/huggingface/hub
- HF_ASSETS_CACHE: /home/wauplin/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/wauplin/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10

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