login

Author: asomoza

benchmarks/README.md

@@ -31,7 +31,7 @@ pip install -r requirements.txt
 We need to be authenticated to access some of the checkpoints used during benchmarking:
 
 ```sh
-huggingface-cli login
+hf auth login
 ```
 
 We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).

docs/source/en/api/configuration.md

@@ -16,7 +16,7 @@ Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from
 
 <Tip>
 
-To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
+To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
 
 </Tip>
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -31,7 +31,7 @@ _As the model is gated, before using it with diffusers you first need to go to t
 Use the command below to log in:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 <Tip>

docs/source/en/training/cogvideox.md

@@ -145,7 +145,7 @@ When running `accelerate config`, if you use torch.compile, there can be dramati
 If you would like to push your model to the Hub after training is completed with a neat model card, make sure you're logged in:
 
 ```bash
-huggingface-cli login
+hf auth login
 
 # Alternatively, you could upload your model manually using:
 # huggingface-cli upload my-cool-account-name/my-cool-lora-name /path/to/awesome/lora

docs/source/en/training/create_dataset.md

@@ -67,7 +67,7 @@ dataset = load_dataset(
 Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
 
 ```python
-# assuming you have ran the huggingface-cli login command in a terminal
+# assuming you have ran the hf auth login command in a terminal
 dataset.push_to_hub("name_of_your_dataset")
 
 # if you want to push to a private repo, simply pass private=True:

docs/source/en/tutorials/basic_training.md

@@ -42,7 +42,7 @@ We encourage you to share your model with the community, and in order to do that
 Or login in from the terminal:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 Since the model checkpoints are quite large, install [Git-LFS](https://git-lfs.com/) to version these large files:

docs/source/ko/optimization/mps.md

@@ -37,7 +37,7 @@ Diffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple
 
 
 ```python
-# `huggingface-cli login`에 로그인되어 있음을 확인
+# `hf auth login`에 로그인되어 있음을 확인
 from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")

docs/source/ko/training/create_dataset.md

@@ -75,7 +75,7 @@ dataset = load_dataset(
 [push_to_hub(https://huggingface.co/docs/datasets/v2.13.1/en/package_reference/main_classes#datasets.Dataset.push_to_hub) 을 사용해서 Hub에 데이터셋을 업로드 합니다:
 
 ```python
-# 터미널에서 huggingface-cli login 커맨드를 이미 실행했다고 가정합니다
+# 터미널에서 hf auth login 커맨드를 이미 실행했다고 가정합니다
 dataset.push_to_hub("name_of_your_dataset")
 
 # 개인 repo로 push 하고 싶다면, `private=True` 을 추가하세요:

docs/source/ko/training/lora.md

@@ -39,7 +39,7 @@ specific language governing permissions and limitations under the License.
 모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](https://huggingface.co/join)하세요):
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 ## Text-to-image

docs/source/ko/tutorials/basic_training.md

@@ -42,7 +42,7 @@ Unconditional 이미지 생성은 학습에 사용된 데이터셋과 유사한
 또는 터미널로 로그인할 수 있습니다:
 
 ```bash
-huggingface-cli login
+hf auth login
 ```
 
 모델 체크포인트가 상당히 크기 때문에 [Git-LFS](https://git-lfs.com/)에서 대용량 파일의 버전 관리를 할 수 있습니다.