[Docs] Improve documentation (#33799)

Co-authored-by: Soren Dreano <soren@numind.ai>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>

docs/models/supported_models.md

@@ -224,13 +224,13 @@ If you prefer, you can use the Hugging Face CLI to [download a model](https://hu
 
 ```bash
 # Download a model
-huggingface-cli download HuggingFaceH4/zephyr-7b-beta
+hf download HuggingFaceH4/zephyr-7b-beta
 
 # Specify a custom cache directory
-huggingface-cli download HuggingFaceH4/zephyr-7b-beta --cache-dir ./path/to/cache
+hf download HuggingFaceH4/zephyr-7b-beta --cache-dir ./path/to/cache
 
 # Download a specific file from a model repo
-huggingface-cli download HuggingFaceH4/zephyr-7b-beta eval_results.json
+hf download HuggingFaceH4/zephyr-7b-beta eval_results.json
 ```
 
 #### List the downloaded models
@@ -239,13 +239,13 @@ Use the Hugging Face CLI to [manage models](https://huggingface.co/docs/huggingf
 
 ```bash
 # List cached models
-huggingface-cli scan-cache
+hf scan-cache
 
 # Show detailed (verbose) output
-huggingface-cli scan-cache -v
+hf scan-cache -v
 
 # Specify a custom cache directory
-huggingface-cli scan-cache --dir ~/.cache/huggingface/hub
+hf scan-cache --dir ~/.cache/huggingface/hub
 ```
 
 #### Delete a cached model
@@ -260,7 +260,7 @@ Use the Hugging Face CLI to interactively [delete downloaded model](https://hugg
 # Please run `pip install huggingface_hub[cli]` to install them.
 
 # Launch the interactive TUI to select models to delete
-$ huggingface-cli delete-cache
+$ hf delete-cache
 ? Select revisions to delete: 1 revisions selected counting for 438.9M.
   ○ None of the following (if selected, nothing will be deleted).
 Model BAAI/bge-base-en-v1.5 (438.9M, used 1 week ago)
@@ -297,7 +297,7 @@ export https_proxy=http://your.proxy.server:port
 - Set the proxy for just the current command:
 
 ```shell
-https_proxy=http://your.proxy.server:port huggingface-cli download <model_name>
+https_proxy=http://your.proxy.server:port hf download <model_name>
 
 # or use vllm cmd directly
 https_proxy=http://your.proxy.server:port  vllm serve <model_name>

examples/offline_inference/openai_batch/README.md

@@ -20,7 +20,7 @@ We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` e
 
 * The examples in this document use `meta-llama/Meta-Llama-3-8B-Instruct`.
     * Create a [user access token](https://huggingface.co/docs/hub/en/security-tokens)
-    * Install the token on your machine (Run `huggingface-cli login`).
+    * Install the token on your machine (Run `hf auth login`).
     * Get access to the gated model by [visiting the model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and agreeing to the terms and conditions.
 
 ## Example 1: Running with a local file

vllm/config/model.py

@@ -247,8 +247,8 @@ class ModelConfig:
     - "mistral" will load the config in mistral format."""
     hf_token: bool | str | None = None
     """The token to use as HTTP bearer authorization for remote files . If
-    `True`, will use the token generated when running `huggingface-cli login`
-    (stored in `~/.huggingface`)."""
+    `True`, will use the token generated when running `hf auth login`
+    (stored in `~/.cache/huggingface/token`)."""
     hf_overrides: HfOverrides = field(default_factory=dict)
     """If a dictionary, contains arguments to be forwarded to the Hugging Face
     config. If a callable, it is called to update the HuggingFace config."""

vllm/entrypoints/llm.py

@@ -170,7 +170,7 @@ class LLM:
             [ParallelConfig][vllm.config.ParallelConfig].
         hf_token: The token to use as HTTP bearer authorization for remote files
             . If `True`, will use the token generated when running
-            `huggingface-cli login` (stored in `~/.huggingface`).
+            `hf auth login` (stored in `~/.cache/huggingface/token`).
         hf_overrides: If a dictionary, contains arguments to be forwarded to the
             HuggingFace config. If a callable, it is called to update the
             HuggingFace config.