4.8 KiB
OpenAI Compatible Server
vLLM provides an HTTP server that implements OpenAI's Completions and Chat API.
You can start the server using Python, or using Docker:
python -m vllm.entrypoints.openai.api_server --model 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.
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="token-abc123",
)
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(completion.choices[0].message)
API Reference
Please see the OpenAI API Reference for more information on the API. We support all parameters except:
- Chat:
tools, andtool_choice. - Completions:
suffix.
vLLM also provides experimental support for OpenAI Vision API compatible inference. See more details in Using VLMs.
Extra Parameters
vLLM supports a set of parameters that are not part of the OpenAI API. In order to use them, you can pass them as extra parameters in the OpenAI client. Or directly merge them into the JSON payload if you are using HTTP call directly.
completion = client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role": "user", "content": "Classify this sentiment: vLLM is wonderful!"}
],
extra_body={
"guided_choice": ["positive", "negative"]
}
)
Extra Parameters for Chat API
The following sampling parameters (click through to see documentation) are supported.
:language: python
:start-after: begin-chat-completion-sampling-params
:end-before: end-chat-completion-sampling-params
The following extra parameters are supported:
:language: python
:start-after: begin-chat-completion-extra-params
:end-before: end-chat-completion-extra-params
Extra Parameters for Completions API
The following sampling parameters (click through to see documentation) are supported.
:language: python
:start-after: begin-completion-sampling-params
:end-before: end-completion-sampling-params
The following extra parameters are supported:
:language: python
:start-after: begin-completion-extra-params
:end-before: end-completion-extra-params
Chat Template
In order for the language model to support chat protocol, vLLM requires the model to include a chat template in its tokenizer configuration. The chat template is a Jinja2 template that specifies how are roles, messages, and other chat-specific tokens are encoded in the input.
An example chat template for NousResearch/Meta-Llama-3-8B-Instruct can be found here
Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those model,
you can manually specify their chat template in the --chat-template parameter with the file path to the chat
template, or the template in string form. Without a chat template, the server will not be able to process chat
and all chat requests will error.
python -m vllm.entrypoints.openai.api_server \
--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 directory here
Command line arguments for the server
:module: vllm.entrypoints.openai.cli_args
:func: create_parser_for_docs
:prog: -m vllm.entrypoints.openai.api_server
Tool calling in the chat completion API
vLLM supports only named function calling in the chat completion API. The tool_choice options auto and required are not yet supported but on the roadmap.
To use a named function you need to define the function in the tools parameter and call it in the tool_choice parameter.
It is the callers responsibility to prompt the model with the tool information, vLLM will not automatically manipulate the prompt. This may change in the future.
vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the tools parameter.
Please refer to the OpenAI API reference documentation for more information.