vllm/docs/source/models/pooling_models.rst

.. _pooling_models:

Pooling Models
==============

vLLM also supports pooling models, including embedding, reranking and reward models.

In vLLM, pooling models implement the :class:`~vllm.model_executor.models.VllmModelForPooling` interface.
These models use a :class:`~vllm.model_executor.layers.Pooler` to extract the final hidden states of the input
before returning them.

.. note::

    We currently support pooling models primarily as a matter of convenience.
    As shown in the :ref:`Compatibility Matrix <compatibility_matrix>`, most vLLM features are not applicable to
    pooling models as they only work on the generation or decode stage, so performance may not improve as much.

Offline Inference
-----------------

The :class:`~vllm.LLM` class provides various methods for offline inference.
See :ref:`Engine Arguments <engine_args>` for a list of options when initializing the model.

For pooling models, we support the following :code:`task` options:

- Embedding (:code:`"embed"` / :code:`"embedding"`)
- Classification (:code:`"classify"`)
- Sentence Pair Scoring (:code:`"score"`)
- Reward Modeling (:code:`"reward"`)

The selected task determines the default :class:`~vllm.model_executor.layers.Pooler` that is used:

- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization.
- Classification: Extract only the hidden states corresponding to the last token, and apply softmax.
- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax.
- Reward Modeling: Extract all of the hidden states and return them directly.

When loading `Sentence Transformers <https://huggingface.co/sentence-transformers>`__ models,
we attempt to override the default pooler based on its Sentence Transformers configuration file (:code:`modules.json`).

You can customize the model's pooling method via the :code:`override_pooler_config` option,
which takes priority over both the model's and Sentence Transformers's defaults.

``LLM.encode``
^^^^^^^^^^^^^^

The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM.
It returns the extracted hidden states directly, which is useful for reward models.

.. code-block:: python

    llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", task="reward")
    output, = llm.encode("Hello, my name is")

    data = output.outputs.data
    print(f"Prompt: {prompt!r} | Data: {data!r}")

``LLM.embed``
^^^^^^^^^^^^^

The :class:`~vllm.LLM.embed` method outputs an embedding vector for each prompt.
It is primarily designed for embedding models.

.. code-block:: python

    llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
    output, = llm.embed("Hello, my name is")

    embeds = output.outputs.embedding
    print(f"Embeddings: {embeds!r} (size={len(embeds)})")

A code example can be found in `examples/offline_inference_embedding.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_embedding.py>`_.

``LLM.classify``
^^^^^^^^^^^^^^^^

The :class:`~vllm.LLM.classify` method outputs a probability vector for each prompt.
It is primarily designed for classification models.

.. code-block:: python

    llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", task="classify")
    output, = llm.classify("Hello, my name is")

    probs = output.outputs.probs
    print(f"Class Probabilities: {probs!r} (size={len(probs)})")

A code example can be found in `examples/offline_inference_classification.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_classification.py>`_.

``LLM.score``
^^^^^^^^^^^^^

The :class:`~vllm.LLM.score` method outputs similarity scores between sentence pairs.
It is primarily designed for `cross-encoder models <https://www.sbert.net/examples/applications/cross-encoder/README.html>`__.
These types of models serve as rerankers between candidate query-document pairs in RAG systems.

.. note::

    vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.
    To handle RAG at a higher level, you should use integration frameworks such as `LangChain <https://github.com/langchain-ai/langchain>`_.

.. code-block:: python

    llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")
    output, = llm.score("What is the capital of France?",
                        "The capital of Brazil is Brasilia.")

    score = output.outputs.score
    print(f"Score: {score}")

A code example can be found in `examples/offline_inference_scoring.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_scoring.py>`_.

Online Inference
----------------

Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference.
Please click on the above link for more details on how to launch the server.

Embeddings API
^^^^^^^^^^^^^^

Our Embeddings API is similar to ``LLM.encode``, accepting both text and :ref:`multi-modal inputs <multimodal_inputs>`.

The text-only API is compatible with `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
so that you can use OpenAI client to interact with it.
A code example can be found in `examples/openai_embedding_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_embedding_client.py>`_.

The multi-modal API is an extension of the `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
that incorporates `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__,
so it is not part of the OpenAI standard. Please see :ref:`this page <multimodal_inputs>` for more details on how to use it.

Score API
^^^^^^^^^

Our Score API is similar to ``LLM.score``.
Please see `this page <../serving/openai_compatible_server.html#score-api-for-cross-encoder-models>`__ for more details on how to use it.