xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-16 02:35:26 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Minor] Fix the format in quick start guide related to Model Scope (#2425)

Browse Source
This commit is contained in:
Zhuohan Li 2024-01-11 19:44:01 -08:00 committed by GitHub
parent 6549aef245
commit f745847ef7
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 8 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
docs/source/getting_started/quickstart.rst
View File

@ -11,6 +11,14 @@ This guide shows how to use vLLM to:
Be sure to complete the :ref:`installation instructions <installation>` before continuing with this guide. Be sure to complete the :ref:`installation instructions <installation>` before continuing with this guide.
.. note::
By default, vLLM downloads model from `HuggingFace <https://huggingface.co/>`_. If you would like to use models from `ModelScope <https://www.modelscope.cn>`_ in the following examples, please set the environment variable:
.. code-block:: shell
export VLLM_USE_MODELSCOPE=True
Offline Batched Inference Offline Batched Inference
------------------------- -------------------------
@ -40,16 +48,6 @@ Initialize vLLM's engine for offline inference with the ``LLM`` class and the `O
llm = LLM(model="facebook/opt-125m") llm = LLM(model="facebook/opt-125m")
Use model from www.modelscope.cn
.. code-block:: shell
export VLLM_USE_MODELSCOPE=True
.. code-block:: python
llm = LLM(model="qwen/Qwen-7B-Chat", revision="v1.1.8", trust_remote_code=True)
Call ``llm.generate`` to generate the outputs. It adds the input prompts to vLLM engine's waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of ``RequestOutput`` objects, which include all the output tokens. Call ``llm.generate`` to generate the outputs. It adds the input prompts to vLLM engine's waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of ``RequestOutput`` objects, which include all the output tokens.
.. code-block:: python .. code-block:: python
@ -77,16 +75,6 @@ Start the server:
$ python -m vllm.entrypoints.api_server $ python -m vllm.entrypoints.api_server
Use model from www.modelscope.cn
.. code-block:: console
$ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.api_server \
$ --model="qwen/Qwen-7B-Chat" \
$ --revision="v1.1.8" \
$ --trust-remote-code
By default, this command starts the server at ``http://localhost:8000`` with the OPT-125M model. By default, this command starts the server at ``http://localhost:8000`` with the OPT-125M model.
Query the model in shell: Query the model in shell:
@ -116,13 +104,6 @@ Start the server:
$ python -m vllm.entrypoints.openai.api_server \ $ python -m vllm.entrypoints.openai.api_server \
$ --model facebook/opt-125m $ --model facebook/opt-125m
Use model from www.modelscope.cn
.. code-block:: console
$ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.openai.api_server \
$ --model="qwen/Qwen-7B-Chat" --revision="v1.1.8" --trust-remote-code
By default, the server uses a predefined chat template stored in the tokenizer. You can override this template by using the ``--chat-template`` argument: 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 .. code-block:: console