Rename Hugging Face CLI from huggingface-cli to hf (#1847)

hanouticelina · Wauplin · web-flow · commit f666b4a1a12e · 2025-07-25T18:19:03.000+02:00
* update hf commands

* Update docs/hub/models-downloading.md

Co-authored-by: Lucain &lt;lucain@huggingface.co&gt;

---------

Co-authored-by: Lucain &lt;lucain@huggingface.co&gt;
diff --git a/docs/hub/datasets-argilla.md b/docs/hub/datasets-argilla.md
@@ -30,7 +30,7 @@ AI teams from companies like [the Red Cross](https://510.global/), [Loris.ai](ht
 First [login with your Hugging Face account](/docs/huggingface_hub/quick-start#login):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Make sure you have `argilla>=2.0.0` installed:
diff --git a/docs/hub/datasets-dask.md b/docs/hub/datasets-dask.md
@@ -13,7 +13,7 @@ Since Dask uses [fsspec](https://filesystem-spec.readthedocs.io) to read and wri
 First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 Then you can [Create a dataset repository](/docs/huggingface_hub/quick-start#create-a-repository), for example using:
diff --git a/docs/hub/datasets-distilabel.md b/docs/hub/datasets-distilabel.md
@@ -17,7 +17,7 @@ The Argilla community uses distilabel to create amazing [datasets](https://huggi
 First [login with your Hugging Face account](/docs/huggingface_hub/quick-start#login):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Make sure you have `distilabel` installed:
diff --git a/docs/hub/datasets-downloading.md b/docs/hub/datasets-downloading.md
@@ -19,10 +19,10 @@ If a dataset on the Hub is tied to a [supported library](./datasets-libraries),
 You can use the [`huggingface_hub`](/docs/huggingface_hub) library to create, delete, update and retrieve information from repos. For example, to download the `HuggingFaceH4/ultrachat_200k` dataset from the command line, run
 
 ```bash
-huggingface-cli download HuggingFaceH4/ultrachat_200k --repo-type dataset
+hf download HuggingFaceH4/ultrachat_200k --repo-type dataset
 ```
 
-See the [huggingface-cli download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-a-dataset-or-a-space) for more information.
+See the [HF CLI download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-a-dataset-or-a-space) for more information.
 
 You can also integrate this into your own library! For example, you can quickly load a CSV dataset with a few lines using Pandas.
 ```py
diff --git a/docs/hub/datasets-duckdb-auth.md b/docs/hub/datasets-duckdb-auth.md
@@ -34,7 +34,7 @@ This command automatically retrieves the stored token from `~/.cache/huggingface
 First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Alternatively, you can set your Hugging Face token as an environment variable:
diff --git a/docs/hub/datasets-fiftyone.md b/docs/hub/datasets-fiftyone.md
@@ -18,7 +18,7 @@ FiftyOne datasets directly from the Hub.
 First [login with your Hugging Face account](/docs/huggingface_hub/quick-start#login):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Make sure you have `fiftyone>=0.24.0` installed:
diff --git a/docs/hub/datasets-gated.md b/docs/hub/datasets-gated.md
@@ -158,7 +158,7 @@ The dataset authors have complete control over dataset access. In particular, th
 To download files from a gated dataset you'll need to be authenticated. In the browser, this is automatic as long as you are logged in with your account. If you are using a script, you will need to provide a [user token](./security-tokens). In the Hugging Face Python ecosystem (`transformers`, `diffusers`, `datasets`, etc.), you can login your machine using the [`huggingface_hub`](/docs/huggingface_hub/index) library and running in your terminal:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Alternatively, you can programmatically login using `login()` in a notebook or a script:
diff --git a/docs/hub/datasets-pandas.md b/docs/hub/datasets-pandas.md
@@ -41,7 +41,7 @@ You can save a pandas DataFrame using `to_csv/to_json/to_parquet` to a local fil
 To save the DataFrame on Hugging Face, you first need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 Then you can [Create a dataset repository](/docs/huggingface_hub/quick-start#create-a-repository), for example using:
diff --git a/docs/hub/datasets-polars-auth.md b/docs/hub/datasets-polars-auth.md
@@ -25,7 +25,7 @@ pl.read_parquet(
 
 ## CLI
 
-Alternatively, you can you use the [Hugging Face CLI](/docs/huggingface_hub/en/guides/cli) to  authenticate. After successfully logging in with `huggingface-cli login` an access token will be stored in the `HF_HOME` directory which defaults to `~/.cache/huggingface`. Polars will then use this token for authentication.
+Alternatively, you can you use the [Hugging Face CLI](/docs/huggingface_hub/en/guides/cli) to  authenticate. After successfully logging in with `hf auth login` an access token will be stored in the `HF_HOME` directory which defaults to `~/.cache/huggingface`. Polars will then use this token for authentication.
 
 If multiple methods are specified, they are prioritized in the following order:
 
diff --git a/docs/hub/datasets-pyarrow.md b/docs/hub/datasets-pyarrow.md
@@ -55,7 +55,7 @@ You can save a pyarrow Table using `pyarrow.parquet.write_table` to a local file
 To save the Table on Hugging Face, you first need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 Then you can [create a dataset repository](/docs/huggingface_hub/quick-start#create-a-repository), for example using:
diff --git a/docs/hub/datasets-spark.md b/docs/hub/datasets-spark.md
@@ -27,7 +27,7 @@ You need to authenticate to Hugging Face to read private/gated dataset repositor
 You can use the CLI for example:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 It's also possible to provide your Hugging Face token with the `HF_TOKEN` environment variable or passing the `token` option to the reader.
diff --git a/docs/hub/datasets-upload-guide-llm.md b/docs/hub/datasets-upload-guide-llm.md
@@ -113,7 +113,7 @@ See https://huggingface.co/docs/hub/storage-limits#repository-limitations-and-re
    - Check for existing README or documentation files that describe the dataset
 
 1. ✓ **Authenticate**:
-   - CLI: `huggingface-cli login`
+   - CLI: `hf auth login`
    - Or use token: `HfApi(token="hf_...")` or set `HF_TOKEN` environment variable
 2. ✓ **Identify your data type**: Check the [Quick Reference](#quick-reference-by-data-type) table above
 3. ✓ **Choose upload method**:
@@ -418,7 +418,7 @@ api.upload_large_folder(folder_path="./data", repo_id="username/dataset", repo_t
 
 | Issue                      | Solution                             |
 | -------------------------- | ------------------------------------ |
-| "Repository not found"     | Run `huggingface-cli login`          |
+| "Repository not found"     | Run `hf auth login`          |
 | Memory errors              | Use `max_shard_size="500MB"`         |
 | Dataset viewer not working | Wait 5-10min, check README.md config |
 | Timeout errors             | Use `multi_commits=True`             |
diff --git a/docs/hub/datasets-usage.md b/docs/hub/datasets-usage.md
@@ -5,7 +5,7 @@ Once you've found an interesting dataset on the Hugging Face Hub, you can load t
 First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 And then you can load a dataset from the Hugging Face Hub using
diff --git a/docs/hub/datasets-webdataset.md b/docs/hub/datasets-webdataset.md
@@ -50,7 +50,7 @@ For example here is how to stream the [timm/imagenet-12k-wds](https://huggingfac
 First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 And then you can stream the dataset with WebDataset:
diff --git a/docs/hub/mlx.md b/docs/hub/mlx.md
@@ -73,7 +73,7 @@ MLX-LM supports popular LLM architectures including LLaMA, Phi-2, Mistral, and Q
 pip install huggingface_hub hf_transfer
 
 export HF_HUB_ENABLE_HF_TRANSFER=1
-huggingface-cli download --local-dir <LOCAL FOLDER PATH> <USER_ID>/<MODEL_NAME>
+hf download --local-dir <LOCAL FOLDER PATH> <USER_ID>/<MODEL_NAME>
 ```
 
 ## Converting and Sharing Models
diff --git a/docs/hub/models-adding-libraries.md b/docs/hub/models-adding-libraries.md
@@ -42,7 +42,7 @@ npm add @huggingface/hub
 Users will need to authenticate once they have successfully installed the `huggingface_hub` library. The easiest way to authenticate is to save the token on the machine. Users can do that from the terminal using the `login()` command:
 
 ```
-huggingface-cli login
+hf auth login
 ```
 
 The command tells them if they are already logged in and prompts them for their token. The token is then validated and saved in their `HF_HOME` directory (defaults to `~/.cache/huggingface/token`). Any script or library interacting with the Hub will use this token when sending requests.
diff --git a/docs/hub/models-downloading.md b/docs/hub/models-downloading.md
@@ -18,9 +18,9 @@ If a model on the Hub is tied to a [supported library](./models-libraries), load
 
 You can use the [`huggingface_hub`](https://github.com/huggingface/huggingface_hub) library to create, delete, update and retrieve information from repos. For example, to download the `HuggingFaceH4/zephyr-7b-beta` model from the command line, run
 ```bash
-huggingface-cli download HuggingFaceH4/zephyr-7b-beta
+hf download HuggingFaceH4/zephyr-7b-beta
 ```
-See the [huggingface-cli download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-an-entire-repository) for more information.
+See the [CLI download documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli#download-an-entire-repository) for more information.
 
 You can also integrate this into your own library. For example, you can quickly load a Scikit-learn model with a few lines.
 ```py
@@ -56,7 +56,7 @@ a Rust-based library developed to speed up file transfers with the Hub.
 
 ```bash
 pip install "huggingface_hub[hf_transfer]"
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ...
+HF_HUB_ENABLE_HF_TRANSFER=1 hf download ...
 ```
 
 <Tip warning={true}>
diff --git a/docs/hub/models-gated.md b/docs/hub/models-gated.md
@@ -172,7 +172,7 @@ The model authors have complete control over model access. In particular, they c
 To download files from a gated model you'll need to be authenticated. In the browser, this is automatic as long as you are logged in with your account. If you are using a script, you will need to provide a [user token](./security-tokens). In the Hugging Face Python ecosystem (`transformers`, `diffusers`, `datasets`, etc.), you can login your machine using the [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/index) library and running in your terminal:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Alternatively, you can programmatically login using `login()` in a notebook or a script:
diff --git a/docs/hub/repositories-getting-started.md b/docs/hub/repositories-getting-started.md
@@ -12,7 +12,7 @@ To be able to push your code to the Hub, you'll need to authenticate somehow. Th
 
 ```bash
 python -m pip install huggingface_hub
-huggingface-cli login
+hf auth login
 ```
 
 **The content in the Getting Started section of this document is also available as a video!**
@@ -116,7 +116,7 @@ git lfs install
 Note that if your files are larger than **5GB** you'll also need to run:
 
 ```bash
-huggingface-cli lfs-enable-largefiles .
+hf lfs-enable-largefiles .
 ```
 
 When you use Hugging Face to create a repository, Hugging Face automatically provides a list of common file extensions for common Machine Learning large files in the `.gitattributes` file, which `git-lfs` uses to efficiently track changes to your large files. However, you might need to add new extensions if your file types are not already handled. You can do so with `git lfs track "*.your_extension"`.
diff --git a/docs/hub/repositories-next-steps.md b/docs/hub/repositories-next-steps.md
@@ -79,7 +79,7 @@ git rebase upstream/main
 
 ```
 git lfs install --force --local # this reinstalls the LFS hooks
-huggingface-cli lfs-enable-largefiles . # needed if some files are bigger than 5GB
+hf lfs-enable-largefiles . # needed if some files are bigger than 5GB
 ```
 
 6. And finally push:
diff --git a/docs/hub/spacy.md b/docs/hub/spacy.md
@@ -86,7 +86,7 @@ python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--local-repo] [
 You can then upload any pipeline packaged with [`spacy package`](https://spacy.io/api/cli#package). Make sure to set `--build wheel` to output a binary .whl file. The uploader will read all metadata from the pipeline package, including the auto-generated pretty `README.md` and the model details available in the `meta.json`.
 
 ```bash
-huggingface-cli login
+hf auth login
 python -m spacy package ./en_ner_fashion ./output --build wheel
 cd ./output/en_ner_fashion-0.0.0/dist
 python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl
diff --git a/docs/hub/timm.md b/docs/hub/timm.md
@@ -104,7 +104,7 @@ Before pushing a model, make sure that you've logged in to Hugging Face:
 
 ```sh
 python -m pip install huggingface_hub
-huggingface-cli login
+hf auth login
 ```
 
 Alternatively, if you prefer working from a Jupyter or Colaboratory notebook, once you've installed `huggingface_hub` you can log in with:
diff --git a/docs/inference-providers/guides/building-first-app.md b/docs/inference-providers/guides/building-first-app.md
@@ -36,7 +36,7 @@ Before we start coding, authenticate with Hugging Face using the CLI:
 
 ```bash
 pip install huggingface_hub
-huggingface-cli login
+hf auth login
 ```
 
 When prompted, paste your Hugging Face token. This handles authentication automatically for all your inference calls. You can generate one from [your settings page](https://huggingface.co/settings/tokens/new?ownUserPermissions=inference.serverless.write&tokenType=fineGrained).
diff --git a/docs/inference-providers/index.md b/docs/inference-providers/index.md
@@ -94,7 +94,7 @@ In your terminal, install the Hugging Face Hub Python client and log in:
 
 ```shell
 pip install huggingface_hub
-huggingface-cli login # get a read token from hf.co/settings/tokens
+hf auth login # get a read token from hf.co/settings/tokens
 ```
 
 You can now use the the client with a Python interpreter:
