huggingface · LysandreJik · Jul 25, 2025 · Jul 24, 2025 · Jul 24, 2025 · Jul 25, 2025
diff --git a/docs/source/ar/custom_models.md b/docs/source/ar/custom_models.md
@@ -280,7 +280,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 الآن لإرسال النموذج إلى Hub، تأكد من تسجيل الدخول. إما تشغيل في المحطة الأوامر الطرفية الخاصة بك:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 أو من دفتر ملاحظات:

diff --git a/docs/source/ar/model_sharing.md b/docs/source/ar/model_sharing.md
@@ -41,7 +41,7 @@ picture-in-picture" allowfullscreen></iframe>
 قبل مشاركة نموذج على Hub، ستحتاج إلى بيانات اعتماد حساب Hugging Face الخاصة بك.  إذا كنت تستخدم منصة الأوامر، فقم بتشغيل الأمر التالي في بيئة افتراضية حيث تم تثبيت 🤗 Transformers. سيقوم هذا الأمر بتخزين رمز الدخول الخاص بك في مجلد تخزين المؤقت لـ Hugging Face (`~/.cache/` بشكل افتراضي):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 إذا كنت تستخدم دفتر ملاحظات مثل Jupyter أو Colaboratory، فتأكد من تثبيت مكتبة [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library). تسمح لك هذه المكتبة بالتفاعل برمجيًا مع Hub.

diff --git a/docs/source/ar/run_scripts.md b/docs/source/ar/run_scripts.md
@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 يمكن لجميع النصوص البرمجية رفع نموذجك النهائي إلى [مركز النماذج](https://huggingface.co/models). تأكد من تسجيل الدخول إلى Hugging Face قبل البدء:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 ثم أضف المعلمة `push_to_hub` إلى النص البرمجي . ستقوم هذه المعلمة بإنشاء مستودع باستخدام اسم مستخدم Hugging Face واسم المجلد المحدد في `output_dir`.

diff --git a/docs/source/de/model_sharing.md b/docs/source/de/model_sharing.md
@@ -56,7 +56,7 @@ Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können
 Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub.

diff --git a/docs/source/de/run_scripts.md b/docs/source/de/run_scripts.md
@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 Alle Skripte können Ihr endgültiges Modell in den [Model Hub](https://huggingface.co/models) hochladen. Stellen Sie sicher, dass Sie bei Hugging Face angemeldet sind, bevor Sie beginnen:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Dann fügen Sie dem Skript das Argument `push_to_hub` hinzu. Mit diesem Argument wird ein Repository mit Ihrem Hugging Face-Benutzernamen und dem in `output_dir` angegebenen Ordnernamen erstellt.

diff --git a/docs/source/en/custom_models.md b/docs/source/en/custom_models.md
@@ -271,7 +271,7 @@ The model is ready to be pushed to the Hub now. Log in to your Hugging Face acco
 <hfoption id="huggingface-CLI">
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 </hfoption>

diff --git a/docs/source/en/model_sharing.md b/docs/source/en/model_sharing.md
@@ -28,7 +28,7 @@ To share a model to the Hub, you need a Hugging Face [account](https://hf.co/joi
 <hfoption id="huggingface-CLI">
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 </hfoption>

diff --git a/docs/source/en/quicktour.md b/docs/source/en/quicktour.md
@@ -49,7 +49,7 @@ notebook_login()
 Make sure the [huggingface_hub[cli]](https://huggingface.co/docs/huggingface_hub/guides/cli#getting-started) package is installed and run the command below. Paste your User Access Token when prompted to log in.
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 </hfoption>

diff --git a/docs/source/en/tasks/semantic_segmentation.md b/docs/source/en/tasks/semantic_segmentation.md
@@ -289,7 +289,7 @@ You could also create and use your own dataset if you prefer to train with the [
           }
      )
 
-     # step 3: push to Hub (assumes you have ran the huggingface-cli login command in a terminal/notebook)
+     # step 3: push to Hub (assumes you have ran the hf auth login command in a terminal/notebook)
      dataset.push_to_hub("your-name/dataset-repo")
 
      # optionally, you can push to a private repo on the Hub

diff --git a/docs/source/es/custom_models.md b/docs/source/es/custom_models.md
@@ -285,7 +285,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 Ahora, para enviar el modelo al Hub, asegúrate de haber iniciado sesión. Ejecuta en tu terminal:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 o desde un _notebook_:

diff --git a/docs/source/es/model_sharing.md b/docs/source/es/model_sharing.md
@@ -56,7 +56,7 @@ Los archivos son editados fácilmente dentro de un repositorio. Incluso puedes o
 Antes de compartir un modelo al Hub necesitarás tus credenciales de Hugging Face. Si tienes acceso a una terminal ejecuta el siguiente comando en el entorno virtual donde 🤗 Transformers esté instalado. Esto guardará tu token de acceso dentro de tu carpeta cache de Hugging Face (~/.cache/ by default):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Si usas un notebook como Jupyter o Colaboratory, asegúrate de tener instalada la biblioteca [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library). Esta biblioteca te permitirá interactuar por código con el Hub.

diff --git a/docs/source/es/run_scripts.md b/docs/source/es/run_scripts.md
@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 Todos los scripts pueden cargar tu modelo final en el [Model Hub](https://huggingface.co/models). Asegúrate de haber iniciado sesión en Hugging Face antes de comenzar:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Luego agrega el argumento `push_to_hub` al script. Este argumento creará un repositorio con tu nombre de usuario Hugging Face y el nombre de la carpeta especificado en `output_dir`.

diff --git a/docs/source/fr/run_scripts_fr.md b/docs/source/fr/run_scripts_fr.md
@@ -327,7 +327,7 @@ python examples/pytorch/summarization/run_summarization.py
 Tous les scripts peuvent télécharger votre modèle final sur le Model Hub. Assurez-vous que vous êtes connecté à Hugging Face avant de commencer :
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Ensuite, ajoutez l'argument `push_to_hub` au script. Cet argument créera un dépôt avec votre nom d'utilisateur Hugging Face et le nom du dossier spécifié dans `output_dir`.

diff --git a/docs/source/it/custom_models.md b/docs/source/it/custom_models.md
@@ -285,7 +285,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 Adesso, per inviare il modello all'Hub, assicurati di aver effettuato l'accesso. Lancia dal tuo terminale:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 O da un notebook:

diff --git a/docs/source/it/model_sharing.md b/docs/source/it/model_sharing.md
@@ -56,7 +56,7 @@ Anche i file possono essere modificati facilmente in un repository ed è possibi
 Prima di condividere un modello nell'Hub, hai bisogno delle tue credenziali di Hugging Face. Se hai accesso ad un terminale, esegui il seguente comando nell'ambiente virtuale in cui è installata la libreria 🤗 Transformers. Questo memorizzerà il tuo token di accesso nella cartella cache di Hugging Face (di default `~/.cache/`):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Se stai usando un notebook come Jupyter o Colaboratory, assicurati di avere la libreria [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) installata. Questa libreria ti permette di interagire in maniera programmatica con l'Hub.

diff --git a/docs/source/it/run_scripts.md b/docs/source/it/run_scripts.md
@@ -324,7 +324,7 @@ python examples/pytorch/summarization/run_summarization.py
 Tutti gli script possono caricare il tuo modello finale al [Model Hub](https://huggingface.co/models). Prima di iniziare, assicurati di aver effettuato l'accesso su Hugging Face:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Poi, aggiungi l'argomento `push_to_hub` allo script. Questo argomento consentirà di creare un repository con il tuo username Hugging Face e la cartella specificata in `output_dir`.

diff --git a/docs/source/ja/custom_models.md b/docs/source/ja/custom_models.md
@@ -270,7 +270,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 モデルをHubに送信するには、ログインしていることを確認してください。ターミナルで次のコマンドを実行します：
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 またはノートブックから：

diff --git a/docs/source/ja/model_sharing.md b/docs/source/ja/model_sharing.md
@@ -56,7 +56,7 @@ Model Hubの組み込みバージョニングはgitおよび[git-lfs](https://gi
 モデルをHubに共有する前に、Hugging Faceの認証情報が必要です。ターミナルへのアクセス権がある場合、🤗 Transformersがインストールされている仮想環境で以下のコマンドを実行します。これにより、アクセストークンがHugging Faceのキャッシュフォルダに保存されます（デフォルトでは `~/.cache/` に保存されます）：
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 JupyterやColaboratoryのようなノートブックを使用している場合、[`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library)ライブラリがインストールされていることを確認してください。

diff --git a/docs/source/ja/run_scripts.md b/docs/source/ja/run_scripts.md
@@ -337,7 +337,7 @@ python examples/pytorch/summarization/run_summarization.py
 すべてのスクリプトは、最終的なモデルを [Model Hub](https://huggingface.co/models) にアップロードできます。開始する前に Hugging Face にログインしていることを確認してください。
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 次に、スクリプトに `push_to_hub` 引数を追加します。この引数は、Hugging Face のユーザー名と `output_dir` で指定したフォルダ名でリポジトリを作成します。

diff --git a/docs/source/ko/custom_models.md b/docs/source/ko/custom_models.md
@@ -277,7 +277,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 터미널에서 다음 코드를 실행해 확인할 수 있습니다:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 주피터 노트북의 경우에는 다음과 같습니다:

diff --git a/docs/source/ko/model_sharing.md b/docs/source/ko/model_sharing.md
@@ -56,7 +56,7 @@ picture-in-picture" allowfullscreen></iframe>
 모델을 허브에 공유하기 전에 Hugging Face 자격 증명이 필요합니다. 터미널에 액세스할 수 있는 경우, 🤗 Transformers가 설치된 가상 환경에서 다음 명령을 실행합니다. 그러면 Hugging Face 캐시 폴더(기본적으로 `~/.cache/`)에 액세스 토큰을 저장합니다:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Jupyter 또는 Colaboratory와 같은 노트북을 사용 중인 경우, [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) 라이브러리가 설치되었는지 확인하세요. 이 라이브러리를 사용하면 API로 허브와 상호 작용할 수 있습니다.

diff --git a/docs/source/ko/run_scripts.md b/docs/source/ko/run_scripts.md
@@ -347,7 +347,7 @@ python examples/pytorch/summarization/run_summarization.py
 모든 스크립트는 최종 모델을 [Model Hub](https://huggingface.co/models)에 업로드할 수 있습니다.
 시작하기 전에 Hugging Face에 로그인했는지 확인하세요:
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 그런 다음 스크립트에 `push_to_hub` 인수를 추가합니다.

diff --git a/docs/source/pt/custom_models.md b/docs/source/pt/custom_models.md
@@ -284,7 +284,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 Agora para enviar o modelo para o Hub, certifique-se de estar logado. Ou execute no seu terminal:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 ou a partir do notebook:

diff --git a/docs/source/pt/run_scripts.md b/docs/source/pt/run_scripts.md
@@ -327,7 +327,7 @@ python examples/pytorch/summarization/run_summarization.py
 Todos os scripts podem enviar seu modelo final para o [Model Hub](https://huggingface.co/models). Certifique-se de estar conectado ao Hugging Face antes de começar:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Em seguida, adicione o argumento `push_to_hub` ao script. Este argumento criará um repositório com seu nome de usuário do Hugging Face e o nome da pasta especificado em `output_dir`.

diff --git a/docs/source/zh/custom_models.md b/docs/source/zh/custom_models.md
@@ -246,7 +246,7 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
 现在要将模型推送到集线器，请确保你已登录。你看可以在终端中运行以下命令：
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 或者在笔记本中运行以下代码：

diff --git a/docs/source/zh/model_sharing.md b/docs/source/zh/model_sharing.md
@@ -56,7 +56,7 @@ Model Hub的内置版本控制基于git和[git-lfs](https://git-lfs.github.com/)
 
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 如果您正在使用像Jupyter或Colaboratory这样的`notebook`，请确保您已安装了[`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library)库。该库允许您以编程方式与Hub进行交互。

diff --git a/docs/source/zh/run_scripts.md b/docs/source/zh/run_scripts.md
@@ -331,7 +331,7 @@ python examples/pytorch/summarization/run_summarization.py
 所有脚本都可以将您的最终模型上传到[Model Hub](https://huggingface.co/models)。在开始之前，请确保您已登录Hugging Face：
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 然后，在脚本中添加`push_to_hub`参数。这个参数会创建一个带有您Hugging Face用户名和`output_dir`中指定的文件夹名称的仓库。

diff --git a/examples/flax/README.md b/examples/flax/README.md
@@ -79,5 +79,5 @@ To specify a given repository name, use the `--hub_model_id` argument. You will
 
 A few notes on this integration:
 
-- you will need to be logged in to the Hugging Face website locally for it to work, the easiest way to achieve this is to run `huggingface-cli login` and then type your username and password when prompted. You can also pass along your authentication token with the `--hub_token` argument.
+- you will need to be logged in to the Hugging Face website locally for it to work, the easiest way to achieve this is to run `hf auth login` and then type your username and password when prompted. You can also pass along your authentication token with the `--hub_token` argument.
 - the `output_dir` you pick will either need to be a new folder or a local clone of the distant repository you are using.
diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -186,7 +186,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/language-modeling/run_bart_dlm_flax.py b/examples/flax/language-modeling/run_bart_dlm_flax.py
@@ -172,7 +172,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py
@@ -173,7 +173,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
@@ -179,7 +179,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/language-modeling/run_t5_mlm_flax.py b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -173,7 +173,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
@@ -159,7 +159,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
@@ -192,7 +192,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
@@ -107,7 +107,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
@@ -155,7 +155,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/flax/vision/run_image_classification.py b/examples/flax/vision/run_image_classification.py
@@ -163,7 +163,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/pytorch/README.md b/examples/pytorch/README.md
@@ -90,7 +90,7 @@ To specify a given repository name, use the `--hub_model_id` argument. You will
 
 A few notes on this integration:
 
-- you will need to be logged in to the Hugging Face website locally for it to work, the easiest way to achieve this is to run `huggingface-cli login` and then type your username and password when prompted. You can also pass along your authentication token with the `--hub_token` argument.
+- you will need to be logged in to the Hugging Face website locally for it to work, the easiest way to achieve this is to run `hf auth login` and then type your username and password when prompted. You can also pass along your authentication token with the `--hub_token` argument.
 - the `output_dir` you pick will either need to be a new folder or a local clone of the distant repository you are using.
 
 ## Distributed training and mixed precision

diff --git a/examples/pytorch/audio-classification/README.md b/examples/pytorch/audio-classification/README.md
@@ -115,10 +115,10 @@ On 4 V100 GPUs (16GB), this script should run in ~1 hour and yield accuracy of *
 $ apt install git-lfs
 ```
 
-2. Log in with your HuggingFace account credentials using `huggingface-cli`
+2. Log in with your HuggingFace account credentials using `hf`
 
 ```bash
-$ huggingface-cli login
+$ hf auth login
 # ...follow the prompts
 ```
 

diff --git a/examples/pytorch/audio-classification/run_audio_classification.py b/examples/pytorch/audio-classification/run_audio_classification.py
@@ -167,7 +167,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )

diff --git a/examples/pytorch/contrastive-image-text/run_clip.py b/examples/pytorch/contrastive-image-text/run_clip.py
@@ -100,7 +100,7 @@ class ModelArguments:
         metadata={
             "help": (
                 "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
-                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+                "generated when running `hf auth login` (stored in `~/.huggingface`)."
             )
         },
     )
-Original file line number
+Diff line change
@@ Expand Up @@
     ```bash
-    huggingface-cli login
+    hf auth login
     ```
     如果您正在使用像Jupyter或Colaboratory这样的`notebook`，请确保您已安装了[`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library)库。该库允许您以编程方式与Hub进行交互。
@@ Expand Down @@