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vllm-v0.6.2/docs/source/getting_started/quickstart.rst
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vllm-v0.6.2/docs/source/getting_started/quickstart.rst
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.. _quickstart:
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==========
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Quickstart
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==========
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This guide will help you quickly get started with vLLM to:
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* :ref:`Run offline batched inference <offline_batched_inference>`
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* :ref:`Run OpenAI-compatible inference <openai_compatible_server>`
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Prerequisites
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--------------
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- OS: Linux
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- Python: 3.9 -- 3.12
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- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
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Installation
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--------------
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You can install vLLM using pip. It's recommended to use `conda <https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html>`_ to create and manage Python environments.
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.. code-block:: console
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$ conda create -n myenv python=3.10 -y
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$ conda activate myenv
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$ pip install vllm
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Please refer to the :ref:`installation documentation <installation>` for more details on installing vLLM.
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.. _offline_batched_inference:
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Offline Batched Inference
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-------------------------
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With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). The example script for this section can be found `here <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py>`__.
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The first line of this example imports the classes :class:`~vllm.LLM` and :class:`~vllm.SamplingParams`:
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- :class:`~vllm.LLM` is the main class for running offline inference with vLLM engine.
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- :class:`~vllm.SamplingParams` specifies the parameters for the sampling process.
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.. code-block:: python
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from vllm import LLM, SamplingParams
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The next section defines a list of input prompts and sampling parameters for text generation. The `sampling temperature <https://arxiv.org/html/2402.05201v1>`_ is set to ``0.8`` and the `nucleus sampling probability <https://en.wikipedia.org/wiki/Top-p_sampling>`_ is set to ``0.95``. You can find more information about the sampling parameters `here <https://docs.vllm.ai/en/stable/dev/sampling_params.html>`__.
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.. code-block:: python
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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The :class:`~vllm.LLM` class initializes vLLM's engine and the `OPT-125M model <https://arxiv.org/abs/2205.01068>`_ for offline inference. The list of supported models can be found :ref:`here <supported_models>`.
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.. code-block:: python
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llm = LLM(model="facebook/opt-125m")
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.. note::
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By default, vLLM downloads models from `HuggingFace <https://huggingface.co/>`_. If you would like to use models from `ModelScope <https://www.modelscope.cn>`_, set the environment variable ``VLLM_USE_MODELSCOPE`` before initializing the engine.
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Now, the fun part! The outputs are generated using ``llm.generate``. It adds the input prompts to the 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 of the output tokens.
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.. code-block:: python
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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.. _openai_compatible_server:
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OpenAI-Compatible Server
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------------------------
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vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
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By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time and implements endpoints such as `list models <https://platform.openai.com/docs/api-reference/models/list>`_, `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_, and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints.
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Run the following command to start the vLLM server with the `Qwen2.5-1.5B-Instruct <https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct>`_ model:
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.. code-block:: console
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$ vllm serve Qwen/Qwen2.5-1.5B-Instruct
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.. note::
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By default, the server uses a predefined chat template stored in the tokenizer. You can learn about overriding it `here <https://github.com/vllm-project/vllm/blob/main/docs/source/serving/openai_compatible_server.md#chat-template>`__.
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This server can be queried in the same format as OpenAI API. For example, to list the models:
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.. code-block:: console
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$ curl http://localhost:8000/v1/models
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You can pass in the argument ``--api-key`` or environment variable ``VLLM_API_KEY`` to enable the server to check for API key in the header.
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OpenAI Completions API with vLLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Once your server is started, you can query the model with input prompts:
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.. code-block:: console
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$ curl http://localhost:8000/v1/completions \
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$ -H "Content-Type: application/json" \
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$ -d '{
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$ "model": "Qwen/Qwen2.5-1.5B-Instruct",
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$ "prompt": "San Francisco is a",
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$ "max_tokens": 7,
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$ "temperature": 0
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$ }'
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Since this server is compatible with OpenAI API, you can use it as a drop-in replacement for any applications using OpenAI API. For example, another way to query the server is via the ``openai`` python package:
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.. code-block:: python
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from openai import OpenAI
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# Modify OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct",
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prompt="San Francisco is a")
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print("Completion result:", completion)
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A more detailed client example can be found `here <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`__.
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OpenAI Chat Completions API with vLLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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vLLM is designed to also support the OpenAI Chat Completions API. The chat interface is a more dynamic, interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations.
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You can use the `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_ endpoint to interact with the model:
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.. code-block:: console
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$ curl http://localhost:8000/v1/chat/completions \
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$ -H "Content-Type: application/json" \
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$ -d '{
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$ "model": "Qwen/Qwen2.5-1.5B-Instruct",
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$ "messages": [
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$ {"role": "system", "content": "You are a helpful assistant."},
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$ {"role": "user", "content": "Who won the world series in 2020?"}
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$ ]
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$ }'
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Alternatively, you can use the ``openai`` python package:
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.. code-block:: python
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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chat_response = client.chat.completions.create(
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model="Qwen/Qwen2.5-1.5B-Instruct",
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Tell me a joke."},
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]
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)
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print("Chat response:", chat_response)
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