quickstart.md

(quickstart)=

# Quickstart

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

- [Offline batched inference](#quickstart-offline)
- [Online serving using OpenAI-compatible server](#quickstart-online)

## Prerequisites

- OS: Linux
- Python: 3.9 -- 3.12

## Installation

If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/project/vllm/) directly.

It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:

```console
uv venv myenv --python 3.12 --seed
source myenv/bin/activate
uv pip install vllm
```

You can also use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) to create and manage Python environments.

```console
conda create -n myenv python=3.12 -y
conda activate myenv
pip install vllm
```

:::{note}
For non-CUDA platforms, please refer [here](#installation-index) for specific instructions on how to install vLLM.
:::

(quickstart-offline)=

## Offline Batched Inference

With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: <gh-file:examples/offline_inference/basic/basic.py>

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

- {class}`~vllm.LLM` is the main class for running offline inference with vLLM engine.
- {class}`~vllm.SamplingParams` specifies the parameters for the sampling process.

```python
from vllm import LLM, SamplingParams
```

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](#sampling-params).

```python
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](https://arxiv.org/abs/2205.01068) for offline inference. The list of supported models can be found [here](#supported-models).

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

:::{note}
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.
:::

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.

```python
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}")
```

(quickstart-online)=

## 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](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.

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:

```console
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](#chat-template).
:::

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

```console
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:

```console
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:

```python
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: <gh-file:examples/online_serving/openai_completion_client.py>

### 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](https://platform.openai.com/docs/api-reference/chat/completions/create) endpoint to interact with the model:

```console
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:

```python
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)
```

## On Attention Backends

Currently, vLLM supports multiple backends for efficient Attention computation across different platforms and accelerator architectures. It automatically selects the most performant backend compatible with your system and model specifications.

If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options: `FLASH_ATTN`, `FLASHINFER` or `XFORMERS`.

```{attention}
There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [Dockerfile](https://github.com/vllm-project/vllm/blob/main/Dockerfile) for instructions on how to install it.
```