#
Command Line Interface (CLI)

The `huggingface_hub` Python package comes with a built-in CLI called `hf`. This tool allows you to interact with the Hugging Face Hub directly from a terminal. For example, you can log in 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.

> This guide covers the most important features of the `hf` CLI. For a complete reference of all commands and options, see the CLI reference.

##
Getting started

First of all, let’s install the CLI:

    >>> pip install -U "huggingface_hub"

> The CLI ships with the core `huggingface_hub` package.

Alternatively, you can install the `hf` CLI with a single command:

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"

Alternatively, you can install the `hf` CLI with a single command:

On macOS and Linux:

    >>> curl -LsSf https://hf.co/cli/install.sh | sh

On Windows:

    >>> powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"

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

    >>> hf --help
    Usage: hf [OPTIONS] COMMAND [ARGS]...

      Hugging Face Hub CLI

    Options:
      --install-completion  Install completion for the current shell.
      --show-completion     Show completion for the current shell, to copy it or
                            customize the installation.
      --help                Show this message and exit.

    Commands:
      auth                 Manage authentication (login, logout, etc.).
      cache                Manage local cache directory.
      download             Download files from the Hub.
      env                  Print information about the environment.
      jobs                 Run and manage Jobs on the Hub.
      repo                 Manage repos on the Hub.
      repo-files           Manage files in a repo on the Hub.
      upload               Upload a file or a folder to the Hub.
      upload-large-folder  Upload a large folder to the Hub.
      version              Print information about the hf version.

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: hf`, 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, `hf upload --help` provides more information on how to upload files using the CLI.

###
Other installation methods

####
Using uv

You can install and run the `hf` CLI with uv.

Make sure uv is installed (adds `uv` and `uvx` to your PATH):

    >>> curl -LsSf https://astral.sh/uv/install.sh | sh

Then install the CLI globally and use it anywhere:

    >>> uv tool install "huggingface_hub"
    >>> hf auth whoami

Alternatively, run the CLI ephemerally with `uvx` (no global install):

    >>> uvx --from huggingface_hub hf auth whoami

####
Using Homebrew

You can also install the CLI using Homebrew:

    >>> brew install huggingface-cli

Check out the Homebrew huggingface page here for more details.

##
hf auth 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:

    >>> hf auth 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 log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .
    Enter your token (input will not be visible):
    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
    >>> hf auth login --token $HF_TOKEN --add-to-git-credential
    Token is valid (permission: write).
    The token `token_name` has been saved to /home/wauplin/.cache/huggingface/stored_tokens
    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
    The current active token is: `token_name`

For more details about authentication, check out this section.

##
hf auth whoami

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

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

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

##
hf auth logout

This command logs you out. In practice, it will delete all tokens stored on your machine. If you want to remove a specific token, you can specify the token name as an argument.

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.

##
hf download

Use the `hf 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:

    hf download --help

###
Download a single file

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

    >>> hf 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.

To download a file located in a subdirectory of the repo, you should provide the path of the file in the repo in posix format like this:

    >>> hf download HiDream-ai/HiDream-I1-Full text_encoder/model.safetensors

###
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:

    >>> hf 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:

    >>> hf 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:

    >>> hf 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
    >>> hf download HuggingFaceH4/ultrachat_200k --repo-type dataset

    # https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
    >>> hf 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:

    >>> hf 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.

A `.cache/huggingface/` folder is created at the root of your local directory containing metadata about the downloaded files. This prevents re-downloading files if they’re already up-to-date. If the metadata has changed, then the new file version is downloaded. This makes the `local-dir` optimized for pulling only the latest changes.

> For more details on how downloading to a local file works, check out the download guide.

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

###
Dry-run mode

In some cases, you would like to check which files would be downloaded before actually downloading them. You can check this using the `--dry-run` parameter. It lists all files to download on the repo and checks whether they are already downloaded or not. This gives an idea of how many files have to be downloaded and their sizes.

    >>> hf download openai-community/gpt2 --dry-run
    [dry-run] Fetching 26 files: 100%|█████████████| 26/26 [00:04<00:00,  6.26it/s]
    [dry-run] Will download 11 files (out of 26) totalling 5.6G.
    File                              Bytes to download
    --------------------------------- -----------------
    .gitattributes                    -
    64-8bits.tflite                   125.2M
    64-fp16.tflite                    248.3M
    64.tflite                         495.8M
    README.md                         -
    config.json                       -
    flax_model.msgpack                497.8M
    generation_config.json            -
    merges.txt                        -
    model.safetensors                 548.1M
    onnx/config.json                  -
    onnx/decoder_model.onnx           653.7M
    onnx/decoder_model_merged.onnx    655.2M
    onnx/decoder_with_past_model.onnx 653.7M
    onnx/generation_config.json       -
    onnx/merges.txt                   -
    onnx/special_tokens_map.json      -
    onnx/tokenizer.json               -
    onnx/tokenizer_config.json        -
    onnx/vocab.json                   -
    pytorch_model.bin                 548.1M
    rust_model.ot                     702.5M
    tf_model.h5                       497.9M
    tokenizer.json                    -
    tokenizer_config.json             -
    vocab.json                        -

For more details, check out the download guide.

###
Specify cache directory

If not using `--local-dir`, all files will be downloaded by default to the cache directory defined by the `HF_HOME` environment variable. You can specify a custom cache using `--cache-dir`:

    >>> hf 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 `hf auth login`) will be used. If you want to authenticate explicitly, use the `--token` option:

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

###
Quiet mode

By default, the `hf 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.

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

###
Download timeout

On machines with slow connections, you might encounter timeout issues like this one:

    `httpx.TimeoutException: (TimeoutException("HTTPSConnectionPool(host='cdn-lfs-us-1.huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: a33d910c-84c6-4514-8362-c705e2039d38)')`

To mitigate this issue, you can set the `HF_HUB_DOWNLOAD_TIMEOUT` environment variable to a higher value (default is 10):

    export HF_HUB_DOWNLOAD_TIMEOUT=30

For more details, check out the environment variables reference. And rerun your download command.

##
hf upload

Use the `hf 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:

    >>> hf upload --help

###
Upload an entire folder

The default usage for this command is:

    # Usage:  hf upload [repo_id] [local_path] [path_in_repo]

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

    >>> hf 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:

    >>> hf 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:

    >>> hf 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:

    >>> hf 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:

    >>> hf 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)
    >>> hf 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:

    >>> hf 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`:

    >>> hf 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
    >>> hf 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
    >>> hf 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
    hf 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

    >>> hf 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 `hf auth login`) will be used. If you want to authenticate explicitly, use the `--token` option:

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

###
Quiet mode

By default, the `hf 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.

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

##
hf repo

`hf repo` lets you create, delete, move repositories and update their settings on the Hugging Face Hub. It also includes subcommands to manage branches and tags.

###
Create a repo

    >>> hf repo create Wauplin/my-cool-model
    Successfully created Wauplin/my-cool-model on the Hub.
    Your repo is now available at https://huggingface.co/Wauplin/my-cool-model

Create a private dataset or a Space:

    >>> hf repo create my-cool-dataset --repo-type dataset --private
    >>> hf repo create my-gradio-space --repo-type space --space-sdk gradio

Use `--exist-ok` if the repo may already exist, and `--resource-group-id` to target an Enterprise resource group.

###
Delete a repo

    >>> hf repo delete Wauplin/my-cool-model

Datasets and Spaces:

    >>> hf repo delete my-cool-dataset --repo-type dataset
    >>> hf repo delete my-gradio-space --repo-type space

###
Move a repo

    >>> hf repo move old-namespace/my-model new-namespace/my-model

###
Update repo settings

    >>> hf repo settings Wauplin/my-cool-model --gated auto
    >>> hf repo settings Wauplin/my-cool-model --private true
    >>> hf repo settings Wauplin/my-cool-model --private false

- `--gated`: one of `auto`, `manual`, `false`
- `--private true|false`: set repository privacy

###
Manage branches

    >>> hf repo branch create Wauplin/my-cool-model dev
    >>> hf repo branch create Wauplin/my-cool-model release-1 --revision refs/pr/104
    >>> hf repo branch delete Wauplin/my-cool-model dev

> All commands accept `--repo-type` (one of `model`, `dataset`, `space`) and `--token` if you need to authenticate explicitly. Use `--help` on any command to see all options.

##
hf repo-files

If you want to delete files from a Hugging Face repository, use the `hf repo-files` command.

###
Delete files

The `hf repo-files delete <repo_id>` sub-command allows you to delete files from a repository. Here are some usage examples.

Delete a folder :

    >>> hf repo-files delete Wauplin/my-cool-model folder/
    Files correctly deleted from repo. Commit: https://huggingface.co/Wauplin/my-cool-mo...

Delete multiple files:

    >>> hf repo-files delete Wauplin/my-cool-model file.txt folder/pytorch_model.bin
    Files correctly deleted from repo. Commit: https://huggingface.co/Wauplin/my-cool-mo...

Use Unix-style wildcards to delete sets of files:

    >>> hf repo-files delete Wauplin/my-cool-model "*.txt" "folder/*.bin"
    Files correctly deleted from repo. Commit: https://huggingface.co/Wauplin/my-cool-mo...

###
Specify a token

To delete files from a repo you must be authenticated and authorized. By default, the token saved locally (using `hf auth login`) will be used. If you want to authenticate explicitly, use the `--token` option:

    >>> hf repo-files delete --token=hf_**** Wauplin/my-cool-model file.txt

##
hf cache ls

Use `hf cache ls` to inspect what is stored locally in your Hugging Face cache. By default it aggregates information by repository:

    >>> hf cache ls
    ID                          SIZE     LAST_ACCESSED LAST_MODIFIED REFS
    --------------------------- -------- ------------- ------------- -----------
    dataset/nyu-mll/glue          157.4M 2 days ago    2 days ago    main script
    model/LiquidAI/LFM2-VL-1.6B     3.2G 4 days ago    4 days ago    main
    model/microsoft/UserLM-8b      32.1G 4 days ago    4 days ago    main

    Found 3 repo(s) for a total of 5 revision(s) and 35.5G on disk.

Add `--revisions` to drill down to specific snapshots, and chain filters to focus on what matters:

    >>> hf cache ls --filter "size>30g" --revisions
    ID                        REVISION                                 SIZE     LAST_MODIFIED REFS
    ------------------------- ---------------------------------------- -------- ------------- ----
    model/microsoft/UserLM-8b be8f2069189bdf443e554c24e488ff3ff6952691    32.1G 4 days ago    main

    Found 1 repo(s) for a total of 1 revision(s) and 32.1G on disk.

The command supports several output formats for scripting: `--format json` prints structured objects, `--format csv` writes comma-separated rows, and `--quiet` prints only IDs. Use `--sort` to order entries by `accessed`, `modified`, `name`, or `size` (append `:asc` or `:desc` to control order), and `--limit` to restrict results to the top N entries. Combine these with `--cache-dir` to target alternative cache locations. See the Manage your cache guide for advanced workflows.

Delete cache entries selected with `hf cache ls --q` by piping the IDs into `hf cache rm`:

    >>> hf cache rm $(hf cache ls --filter "accessed>1y" -q) -y
    About to delete 2 repo(s) totalling 5.31G.
      - model/meta-llama/Llama-3.2-1B-Instruct (entire repo)
      - model/hexgrad/Kokoro-82M (entire repo)
    Delete repo: ~/.cache/huggingface/hub/models--meta-llama--Llama-3.2-1B-Instruct
    Delete repo: ~/.cache/huggingface/hub/models--hexgrad--Kokoro-82M
    Cache deletion done. Saved 5.31G.
    Deleted 2 repo(s) and 2 revision(s); freed 5.31G.

##
hf cache rm

`hf cache rm` removes cached repositories or individual revisions. Pass one or more repo IDs (`model/bert-base-uncased`) or revision hashes:

    >>> hf cache rm model/LiquidAI/LFM2-VL-1.6B
    About to delete 1 repo(s) totalling 3.2G.
      - model/LiquidAI/LFM2-VL-1.6B (entire repo)
    Proceed with deletion? [y/N]: y
    Delete repo: ~/.cache/huggingface/hub/models--LiquidAI--LFM2-VL-1.6B
    Cache deletion done. Saved 3.2G.
    Deleted 1 repo(s) and 2 revision(s); freed 3.2G.

Mix repositories and specific revisions in the same call. Use `--dry-run` to preview the impact, or `--yes` to skip the confirmation prompt—handy in automated scripts:

    >>> hf cache rm model/t5-small 8f3ad1c --dry-run
    About to delete 1 repo(s) and 1 revision(s) totalling 1.1G.
      - model/t5-small:
          8f3ad1c [main] 1.1G
    Dry run: no files were deleted.

When working outside the default cache location, pair the command with `--cache-dir PATH`.

##
hf cache prune

`hf cache prune` is a convenience shortcut that deletes every detached (unreferenced) revision in your cache. This keeps only revisions that are still reachable through a branch or tag:

    >>> hf cache prune
    About to delete 3 unreferenced revision(s) (2.4G total).
      - model/t5-small:
          1c610f6b [refs/pr/1] 820.1M
          d4ec9b72 [(detached)] 640.5M
      - dataset/google/fleurs:
          2b91c8dd [(detached)] 937.6M
    Proceed? [y/N]: y
    Deleted 3 unreferenced revision(s); freed 2.4G.

As with the other cache commands, `--dry-run`, `--yes`, and `--cache-dir` are available. Refer to the Manage your cache guide for more examples.

##
hf cache verify

Use `hf cache verify` to validate local files against their checksums on the Hub. You can verify either a cache snapshot or a regular local directory.

Examples:

    # Verify main revision of a model in cache
    >>> hf cache verify deepseek-ai/DeepSeek-OCR

    # Verify a specific revision
    >>> hf cache verify deepseek-ai/DeepSeek-OCR --revision refs/pr/5
    >>> hf cache verify deepseek-ai/DeepSeek-OCR --revision ef93bf4a377c5d5ed9dca78e0bc4ea50b26fe6a4

    # Verify a private repo
    >>> hf cache verify me/private-model --token hf_***

    # Verify a dataset
    >>> hf cache verify karpathy/fineweb-edu-100b-shuffle --repo-type dataset

    # Verify files in a local directory
    >>> hf cache verify deepseek-ai/DeepSeek-OCR --local-dir /path/to/repo

By default, the command warns about missing or extra files. Use flags to turn these warnings into errors:

    >>> hf cache verify deepseek-ai/DeepSeek-OCR --fail-on-missing-files --fail-on-extra-files

On success, you will see a summary:

    ✅ Verified 13 file(s) for 'deepseek-ai/DeepSeek-OCR' (model) in ~/.cache/huggingface/hub/models--meta-llama--Llama-3.2-1B-Instruct/snapshots/9213176726f574b556790deb65791e0c5aa438b6
      All checksums match.

If mismatches are detected, the command prints a detailed list and exits with a non-zero status.

##
hf repo tag create

The `hf repo tag create` command allows you to tag, untag, and list tags for repositories.

###
Tag a model

To tag a repo, you need to provide the `repo_id` and the `tag` name:

    >>> hf repo tag create Wauplin/my-cool-model v1.0
    You are about to create tag v1.0 on model Wauplin/my-cool-model
    Tag v1.0 created on Wauplin/my-cool-model

###
Tag a model at a specific revision

If you want to tag a specific revision, you can use the `--revision` option. By default, the tag will be created on the `main` branch:

    >>> hf repo tag create Wauplin/my-cool-model v1.0 --revision refs/pr/104
    You are about to create tag v1.0 on model Wauplin/my-cool-model
    Tag v1.0 created on Wauplin/my-cool-model

###
Tag a dataset or a Space

If you want to tag a dataset or Space, you must specify the `--repo-type` option:

    >>> hf repo tag create bigcode/the-stack v1.0 --repo-type dataset
    You are about to create tag v1.0 on dataset bigcode/the-stack
    Tag v1.0 created on bigcode/the-stack

###
List tags

To list all tags for a repository, use the `-l` or `--list` option:

    >>> hf repo tag create Wauplin/gradio-space-ci -l --repo-type space
    Tags for space Wauplin/gradio-space-ci:
    0.2.2
    0.2.1
    0.2.0
    0.1.2
    0.0.2
    0.0.1

###
Delete a tag

To delete a tag, use the `-d` or `--delete` option:

    >>> hf repo tag create -d Wauplin/my-cool-model v1.0
    You are about to delete tag v1.0 on model Wauplin/my-cool-model
    Proceed? [Y/n] y
    Tag v1.0 deleted on Wauplin/my-cool-model

You can also pass `-y` to skip the confirmation step.

##
hf env

The `hf 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.

    >>> hf env

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

    - huggingface_hub version: 1.0.0.rc6
    - Platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.35
    - Python version: 3.11.14
    - Running in iPython ?: No
    - Running in notebook ?: No
    - Running in Google Colab ?: No
    - Running in Google Colab Enterprise ?: No
    - Token path ?: /home/wauplin/.cache/huggingface/token
    - Has saved token ?: True
    - Who am I ?: Wauplin
    - Configured git credential helpers: store
    - Installation method: unknown
    - Torch: N/A
    - httpx: 0.28.1
    - hf_xet: 1.1.10
    - gradio: 5.41.1
    - tensorboard: N/A
    - pydantic: 2.11.7
    - 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_STORED_TOKENS_PATH: /home/wauplin/.cache/huggingface/stored_tokens
    - 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_DISABLE_XET: False
    - HF_HUB_ETAG_TIMEOUT: 10
    - HF_HUB_DOWNLOAD_TIMEOUT: 10

##
hf jobs

Run compute jobs on Hugging Face infrastructure with a familiar Docker-like interface.

`hf jobs` is a command-line tool that lets you run anything on Hugging Face’s infrastructure (including GPUs and TPUs!) with simple commands. Think `docker run`, but for running code on A100s.

    # Directly run Python code
    >>> hf jobs run python:3.12 python -c 'print("Hello from the cloud!")'

    # Use GPUs without any setup
    >>> hf jobs run --flavor a10g-small pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \
    ... python -c "import torch; print(torch.cuda.get_device_name())"

    # Run in an organization account
    >>> hf jobs run --namespace my-org-name python:3.12 python -c "print('Running in an org account')"

    # Run from Hugging Face Spaces
    >>> hf jobs run hf.co/spaces/lhoestq/duckdb duckdb -c "select 'hello world'"

    # Run a Python script with `uv` (experimental)
    >>> hf jobs uv run my_script.py

###
✨ Key Features

- 🐳 Docker-like CLI: Familiar commands (`run`, `ps`, `logs`, `inspect`) to run and manage jobs
- 🔥 Any Hardware: From CPUs to A100 GPUs and TPU pods - switch with a simple flag
- 📦 Run Anything: Use Docker images, HF Spaces, or your custom containers
- 🔐 Simple Auth: Just use your HF token
- 📊 Live Monitoring: Stream logs in real-time, just like running locally
- 💰 Pay-as-you-go: Only pay for the seconds you use

> Hugging Face Jobs are available only to Pro users and Team or Enterprise organizations. Upgrade your plan to get started!

###
Quick Start

####
1. Run your first job

    # Run a simple Python script
    >>> hf jobs run python:3.12 python -c "print('Hello from HF compute!')"

This command runs the job and shows the logs. You can pass `--detach` to run the Job in the background and only print the Job ID.

####
2. Check job status

    # List your running jobs
    >>> hf jobs ps

    # Inspect the status of a job
    >>> hf jobs inspect <job_id>

    # View logs from a job
    >>> hf jobs logs <job_id>

    # Cancel a job
    >>> hf jobs cancel <job_id>

####
3. Run on GPU

You can also run jobs on GPUs or TPUs with the `--flavor` option. For example, to run a PyTorch job on an A10G GPU:

    # Use an A10G GPU to check PyTorch CUDA
    >>> hf jobs run --flavor a10g-small pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \
    ... python -c "import torch; print(f"This code ran with the following GPU: {torch.cuda.get_device_name()}")"

Running this will show the following output!

    This code ran with the following GPU: NVIDIA A10G

That’s it! You’re now running code on Hugging Face’s infrastructure.

###
Common Use Cases

- Model Training: Fine-tune or train models on GPUs (T4, A10G, A100) without managing infrastructure
- Synthetic Data Generation: Generate large-scale datasets using LLMs on powerful hardware
- Data Processing: Process massive datasets with high-CPU configurations for parallel workloads
- Batch Inference: Run offline inference on thousands of samples using optimized GPU setups
- Experiments & Benchmarks: Run ML experiments on consistent hardware for reproducible results
- Development & Debugging: Test GPU code without local CUDA setup

###
Pass Environment variables and Secrets

You can pass environment variables to your job using

    # Pass environment variables
    >>> hf jobs run -e FOO=foo -e BAR=bar python:3.12 python -c "import os; print(os.environ['FOO'], os.environ['BAR'])"

    # Pass an environment from a local .env file
    >>> hf jobs run --env-file .env python:3.12 python -c "import os; print(os.environ['FOO'], os.environ['BAR'])"

    # Pass secrets - they will be encrypted server side
    >>> hf jobs run -s MY_SECRET=psswrd python:3.12 python -c "import os; print(os.environ['MY_SECRET'])"

    # Pass secrets from a local .env.secrets file - they will be encrypted server side
    >>> hf jobs run --secrets-file .env.secrets python:3.12 python -c "import os; print(os.environ['MY_SECRET'])"

> Use `--secrets HF_TOKEN` to pass your local Hugging Face token implicitly. With this syntax, the secret is retrieved from the environment variable. For `HF_TOKEN`, it may read the token file located in the Hugging Face home folder if the environment variable is unset.

###
Hardware

Available `--flavor` options:

- CPU: `cpu-basic`, `cpu-upgrade`
- GPU: `t4-small`, `t4-medium`, `l4x1`, `l4x4`, `a10g-small`, `a10g-large`, `a10g-largex2`, `a10g-largex4`,`a100-large`
- TPU: `v5e-1x1`, `v5e-2x2`, `v5e-2x4`

(updated in 07/2025 from Hugging Face suggested_hardware docs)

###
UV Scripts (Experimental)

Run UV scripts (Python scripts with inline dependencies) on HF infrastructure:

    # Run a UV script (creates temporary repo)
    >>> hf jobs uv run my_script.py

    # Run with persistent repo
    >>> hf jobs uv run my_script.py --repo my-uv-scripts

    # Run with GPU
    >>> hf jobs uv run ml_training.py --flavor gpu-t4-small

    # Pass arguments to script
    >>> hf jobs uv run process.py input.csv output.parquet

    # Add dependencies
    >>> hf jobs uv run --with transformers --with torch train.py

    # Run a script directly from a URL
    >>> hf jobs uv run https://huggingface.co/datasets/username/scripts/resolve/main/example.py

    # Run a command
    >>> hf jobs uv run --with lighteval python -c "import lighteval"

UV scripts are Python scripts that include their dependencies directly in the file using a special comment syntax. This makes them perfect for self-contained tasks that don’t require complex project setups. Learn more about UV scripts in the UV documentation.

###
Scheduled Jobs

Schedule and manage jobs that will run on HF infrastructure.

The schedule should be one of `@annually`, `@yearly`, `@monthly`, `@weekly`, `@daily`, `@hourly`, or a CRON schedule expression (e.g., `"0 9 * * 1"` for 9 AM every Monday).

    # Schedule a job that runs every hour
    >>> hf jobs scheduled run @hourly python:3.12 python -c 'print("This runs every hour!")'

    # Use the CRON syntax
    >>> hf jobs scheduled run "*/5 * * * *" python:3.12 python -c 'print("This runs every 5 minutes!")'

    # Schedule with GPU
    >>> hf jobs scheduled run @hourly --flavor a10g-small pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \
    ... python -c "import torch; print(f"This code ran with the following GPU: {torch.cuda.get_device_name()}")"

    # Schedule a UV script
    >>> hf jobs scheduled uv run @hourly my_script.py

Use the same parameters as `hf jobs run` to pass environment variables, secrets, timeout, etc.

Manage scheduled jobs using

    # List your active scheduled jobs
    >>> hf jobs scheduled ps

    # Inspect the status of a job
    >>> hf jobs scheduled inspect <scheduled_job_id>

    # Suspend (pause) a scheduled job
    >>> hf jobs scheduled suspend <scheduled_job_id>

    # Resume a scheduled job
    >>> hf jobs scheduled resume <scheduled_job_id>

    # Delete a scheduled job
    >>> hf jobs scheduled delete <scheduled_job_id>

##
hf endpoints

Use `hf endpoints` to list, deploy, describe, and manage Inference Endpoints directly from the terminal. The legacy `hf inference-endpoints` alias remains available for compatibility.

    # Lists endpoints in your namespace
    >>> hf endpoints ls

    # Deploy an endpoint from Model Catalog
    >>> hf endpoints catalog deploy --repo openai/gpt-oss-120b --name my-endpoint

    # Deploy an endpoint from the Hugging Face Hub
    >>> hf endpoints deploy my-endpoint --repo gpt2 --framework pytorch --accelerator cpu --instance-size x2 --instance-type intel-icl

    # List catalog entries
    >>> hf endpoints catalog ls

    # Show status and metadata
    >>> hf endpoints describe my-endpoint

    # Pause the endpoint
    >>> hf endpoints pause my-endpoint

    # Delete without confirmation prompt
    >>> hf endpoints delete my-endpoint --yes

> Add `--namespace` to target an organization, `--token` to override authentication.

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