[Doc][CI/Build] Update docs and tests to use vllm serve (#6431)

docs/source/getting_started/quickstart.rst

@@ -73,16 +73,13 @@ Start the server:
 
 .. code-block:: console
 
-    $ python -m vllm.entrypoints.openai.api_server \
-    $     --model facebook/opt-125m
+    $ vllm serve facebook/opt-125m
 
 By default, the server uses a predefined chat template stored in the tokenizer. You can override this template by using the ``--chat-template`` argument:
 
 .. code-block:: console
 
-   $ python -m vllm.entrypoints.openai.api_server \
-   $     --model facebook/opt-125m \
-   $     --chat-template ./examples/template_chatml.jinja
+    $ vllm serve facebook/opt-125m --chat-template ./examples/template_chatml.jinja
 
 This server can be queried in the same format as OpenAI API. For example, list the models:
 

docs/source/models/adding_model.rst

@@ -114,7 +114,7 @@ Just add the following lines in your code:
     from your_code import YourModelForCausalLM
     ModelRegistry.register_model("YourModelForCausalLM", YourModelForCausalLM)
 
-If you are running api server with `python -m vllm.entrypoints.openai.api_server args`, you can wrap the entrypoint with the following code:
+If you are running api server with :code:`vllm serve <args>`, you can wrap the entrypoint with the following code:
 
 .. code-block:: python
 
@@ -124,4 +124,4 @@ If you are running api server with `python -m vllm.entrypoints.openai.api_server
     import runpy
     runpy.run_module('vllm.entrypoints.openai.api_server', run_name='__main__')
 
-Save the above code in a file and run it with `python your_file.py args`.
+Save the above code in a file and run it with :code:`python your_file.py <args>`.

docs/source/models/engine_args.rst

@@ -8,7 +8,7 @@ Below, you can find an explanation of every engine argument for vLLM:
 .. argparse::
     :module: vllm.engine.arg_utils
     :func: _engine_args_parser
-    :prog: -m vllm.entrypoints.openai.api_server
+    :prog: vllm serve
     :nodefaultconst:
 
 Async Engine Arguments
@@ -19,5 +19,5 @@ Below are the additional arguments related to the asynchronous engine:
 .. argparse::
     :module: vllm.engine.arg_utils
     :func: _async_engine_args_parser
-    :prog: -m vllm.entrypoints.openai.api_server
+    :prog: vllm serve
     :nodefaultconst:

docs/source/models/lora.rst

@@ -61,8 +61,7 @@ LoRA adapted models can also be served with the Open-AI compatible vLLM server.
 
 .. code-block:: bash
 
-    python -m vllm.entrypoints.openai.api_server \
-        --model meta-llama/Llama-2-7b-hf \
+    vllm serve meta-llama/Llama-2-7b-hf \
         --enable-lora \
         --lora-modules sql-lora=$HOME/.cache/huggingface/hub/models--yard1--llama-2-7b-sql-lora-test/snapshots/0dfa347e8877a4d4ed19ee56c140fa518470028c/
 

docs/source/models/vlm.rst

@@ -94,9 +94,7 @@ Below is an example on how to launch the same ``llava-hf/llava-1.5-7b-hf`` with
 
 .. code-block:: bash
 
-    python -m vllm.entrypoints.openai.api_server \
-        --model llava-hf/llava-1.5-7b-hf \
-        --chat-template template_llava.jinja
+    vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
 
 .. important::
     We have removed all vision language related CLI args in the ``0.5.1`` release. **This is a breaking change**, so please update your code to follow

docs/source/serving/deploying_with_dstack.rst

@@ -40,7 +40,7 @@ Next, to provision a VM instance with LLM of your choice(`NousResearch/Llama-2-7
         gpu: 24GB
     commands:
         - pip install vllm
-        - python -m vllm.entrypoints.openai.api_server --model $MODEL --port 8000
+        - vllm serve $MODEL --port 8000
     model:
         format: openai
         type: chat

docs/source/serving/distributed_serving.rst

@@ -35,16 +35,14 @@ To run multi-GPU serving, pass in the :code:`--tensor-parallel-size` argument wh
 
 .. code-block:: console
 
-    $ python -m vllm.entrypoints.openai.api_server \
-    $     --model facebook/opt-13b \
+    $ vllm serve facebook/opt-13b \
     $     --tensor-parallel-size 4
 
 You can also additionally specify :code:`--pipeline-parallel-size` to enable pipeline parallelism. For example, to run API server on 8 GPUs with pipeline parallelism and tensor parallelism:
 
 .. code-block:: console
 
-    $ python -m vllm.entrypoints.openai.api_server \
-    $     --model gpt2 \
+    $ vllm serve gpt2 \
     $     --tensor-parallel-size 4 \
     $     --pipeline-parallel-size 2 \
     $     --distributed-executor-backend ray

docs/source/serving/openai_compatible_server.md

@@ -4,7 +4,7 @@ vLLM provides an HTTP server that implements OpenAI's [Completions](https://plat
 
 You can start the server using Python, or using [Docker](deploying_with_docker.rst):
 ```bash
-python -m vllm.entrypoints.openai.api_server --model NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
+vllm serve NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
 ```
 
 To call the server, you can use the official OpenAI Python client library, or any other HTTP client.
@@ -97,9 +97,7 @@ template, or the template in string form. Without a chat template, the server wi
 and all chat requests will error.
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
-  --model ... \
-  --chat-template ./path-to-chat-template.jinja
+vllm serve <model> --chat-template ./path-to-chat-template.jinja
 ```
 
 vLLM community provides a set of chat templates for popular models. You can find them in the examples
@@ -110,7 +108,7 @@ directory [here](https://github.com/vllm-project/vllm/tree/main/examples/)
 ```{argparse}
 :module: vllm.entrypoints.openai.cli_args
 :func: create_parser_for_docs
-:prog: -m vllm.entrypoints.openai.api_server
+:prog: vllm serve
 ```
 
 ## Tool calling in the chat completion API