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 exitIf 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.
_| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|
_| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
_|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|
_| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
_| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|
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 successfulAlternatively, 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 successfulFor more details about authentication, check out this section.
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,HFSmolClusterIf 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 --helpDownload 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/462165984030d82259a11f4367a4eed129e94a7bDownload 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 --helpUpload 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
...Note: if revision does not exist and --create-pr is not set, a branch will be created automatically from the main branch.
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=10Specify 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/mainSpecify 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