vLLM provides an HTTP server that implements OpenAI's [Completions API](https://platform.openai.com/docs/api-reference/completions), [Chat API](https://platform.openai.com/docs/api-reference/chat), and more! This functionality lets you serve models and interact with them using an HTTP client.
vLLM provides an HTTP server that implements OpenAI's [Completions API](https://platform.openai.com/docs/api-reference/completions), [Chat API](https://platform.openai.com/docs/api-reference/chat), and more! This functionality lets you serve models and interact with them using an HTTP client.
In your terminal, you can [install](../getting_started/installation.md) vLLM, then start the server with the [`vllm serve`](#vllm-serve) command. (You can also use our [Docker](#deployment-docker) image.)
In your terminal, you can [install](../getting_started/installation/README.md) vLLM, then start the server with the [`vllm serve`][serve-args] command. (You can also use our [Docker][deployment-docker] image.)
```bash
```bash
vllm serve NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
To call the server, in your preferred text editor, create a script that uses an HTTP client. Include any messages that you want to send to the model. Then run that script. Below is an example script using the [official OpenAI Python client](https://github.com/openai/openai-python).
To call the server, in your preferred text editor, create a script that uses an HTTP client. Include any messages that you want to send to the model. Then run that script. Below is an example script using the [official OpenAI Python client](https://github.com/openai/openai-python).
...
@@ -20,56 +23,56 @@ client = OpenAI(
...
@@ -20,56 +23,56 @@ client = OpenAI(
)
)
completion=client.chat.completions.create(
completion=client.chat.completions.create(
model="NousResearch/Meta-Llama-3-8B-Instruct",
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
messages=[
{"role":"user","content":"Hello!"}
{"role":"user","content":"Hello!"}
]
]
)
)
print(completion.choices[0].message)
print(completion.choices[0].message)
```
```
:::{tip}
!!! tip
vLLM supports some parameters that are not supported by OpenAI, `top_k` for example.
vLLM supports some parameters that are not supported by OpenAI, `top_k` for example.
You can pass these parameters to vLLM using the OpenAI client in the `extra_body` parameter of your requests, i.e. `extra_body={"top_k": 50}` for `top_k`.
You can pass these parameters to vLLM using the OpenAI client in the `extra_body` parameter of your requests, i.e. `extra_body={"top_k": 50}` for `top_k`.
:::
:::{important}
!!! warning
By default, the server applies `generation_config.json` from the Hugging Face model repository if it exists. This means the default values of certain sampling parameters can be overridden by those recommended by the model creator.
By default, the server applies `generation_config.json` from the Hugging Face model repository if it exists. This means the default values of certain sampling parameters can be overridden by those recommended by the model creator.
To disable this behavior, please pass `--generation-config vllm` when launching the server.
To disable this behavior, please pass `--generation-config vllm` when launching the server.
@@ -433,17 +363,137 @@ It consists of two endpoints:
...
@@ -433,17 +363,137 @@ It consists of two endpoints:
-`/tokenize` corresponds to calling `tokenizer.encode()`.
-`/tokenize` corresponds to calling `tokenizer.encode()`.
-`/detokenize` corresponds to calling `tokenizer.decode()`.
-`/detokenize` corresponds to calling `tokenizer.decode()`.
(pooling-api)=
[](){ #pooling-api }
### Pooling API
### Pooling API
Our Pooling API encodes input prompts using a [pooling model](../models/pooling_models.md) and returns the corresponding hidden states.
Our Pooling API encodes input prompts using a [pooling model](../models/pooling_models.md) and returns the corresponding hidden states.
The input format is the same as [Embeddings API](#embeddings-api), but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
The input format is the same as [Embeddings API][embeddings-api], but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
Our Classification API directly supports Hugging Face sequence-classification models such as [ai21labs/Jamba-tiny-reward-dev](https://huggingface.co/ai21labs/Jamba-tiny-reward-dev) and [jason9693/Qwen2.5-1.5B-apeach](https://huggingface.co/jason9693/Qwen2.5-1.5B-apeach).
We automatically wrap any other transformer via `as_classification_model()`, which pools on the last token, attaches a `RowParallelLinear` head, and applies a softmax to produce per-class probabilities.
<p>You are viewing the latest developer preview docs. <ahref="https://docs.vllm.ai/en/stable/">Click here</a> to view docs for the latest stable release.</p>
