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Quickstart

This guide will help you quickly get started with vLLM to:

Be sure to complete the Gaudi installation instructions before continuing with this guide. Prerequisites -------------- - OS: Linux - Python: 3.9 -- 3.12 - GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)

Installation

You can install vLLM using pip. It's recommended to use conda to create and manage Python environments.

$ conda create -n myenv python=3.10 -y
$ conda activate myenv
$ pip install vllm

Please refer to the :ref:`installation documentation <installation>` for more details on installing vLLM.

Offline Batched Inference

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.

The first line of this example imports the classes :class:`~vllm.LLM` and :class:`~vllm.SamplingParams`:

from vllm import LLM, SamplingParams

The next section defines a list of input prompts and sampling parameters for text generation. The sampling temperature is set to 0.8 and the nucleus sampling probability is set to 0.95. You can find more information about the sampling parameters here.

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

The :class:`~vllm.LLM` class initializes vLLM's engine and the OPT-125M model for offline inference. The list of supported models can be found :ref:`here <supported_models>`.

llm = LLM(model="facebook/opt-125m")

Note

By default, vLLM downloads models from HuggingFace. If you would like to use models from ModelScope, set the environment variable VLLM_USE_MODELSCOPE before initializing the engine.

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.

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

OpenAI-Compatible Server

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. 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, create chat completion, and create completion endpoints.

Run the following command to start the vLLM server with the Qwen2.5-1.5B-Instruct model:

$ vllm serve Qwen/Qwen2.5-1.5B-Instruct

Note

By default, the server uses a predefined chat template stored in the tokenizer. You can learn about overriding it here.

This server can be queried in the same format as OpenAI API. For example, to list the models:

$ curl http://localhost:8000/v1/models

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.

OpenAI Completions API with vLLM

Once your server is started, you can query the model with input prompts:

$ curl http://localhost:8000/v1/completions \
$     -H "Content-Type: application/json" \
$     -d '{
$         "model": "Qwen/Qwen2.5-1.5B-Instruct",
$         "prompt": "San Francisco is a",
$         "max_tokens": 7,
$         "temperature": 0
$     }'

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:

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct",
                                      prompt="San Francisco is a")
print("Completion result:", completion)

A more detailed client example can be found here.

OpenAI Chat Completions API with vLLM

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.

You can use the create chat completion endpoint to interact with the model:

$ curl http://localhost:8000/v1/chat/completions \
$     -H "Content-Type: application/json" \
$     -d '{
$         "model": "Qwen/Qwen2.5-1.5B-Instruct",
$         "messages": [
$             {"role": "system", "content": "You are a helpful assistant."},
$             {"role": "user", "content": "Who won the world series in 2020?"}
$         ]
$     }'

Alternatively, you can use the openai python package:

from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

chat_response = client.chat.completions.create(
    model="Qwen/Qwen2.5-1.5B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a joke."},
    ]
)
print("Chat response:", chat_response)