vllm/docs/source/models/pooling_models.md

(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 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. :::

For pooling models, we support the following --task options. The selected option sets the default pooler used to extract the final hidden states:

:::{list-table} :widths: 50 25 25 25 :header-rows: 1

• • Task
  • Pooling Type
  • Normalization
  • Softmax
• • Embedding (embed)
  • LAST
  • ✅︎
  • ❌
• • Classification (classify)
  • LAST
  • ❌
  • ✅︎
• • Sentence Pair Scoring (score)
  • *
  • *
  • *
• • Reward Modeling (reward)
  • ALL
  • ❌
  • ❌ :::

*The default pooler is always defined by the model.

:::{note} If the model's implementation in vLLM defines its own pooler, the default pooler is set to that instead of the one specified in this table. :::

When loading Sentence Transformers models, we attempt to override the default pooler based on its Sentence Transformers configuration file (modules.json).

:::{tip} You can customize the model's pooling method via the --override-pooler-config option, which takes priority over both the model's and Sentence Transformers's defaults. :::

Offline Inference

The {class}~vllm.LLM class provides various methods for offline inference. See Engine Arguments for a list of options when initializing the model.

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.

from vllm import LLM

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

data = output.outputs.data
print(f"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.

from vllm import LLM

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 here: gh-file:examples/offline_inference/basic/embed.py

LLM.classify

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

from vllm import LLM

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 here: gh-file:examples/offline_inference/basic/classify.py

LLM.score

The {class}~vllm.LLM.score method outputs similarity scores between sentence pairs. It is designed for embedding models and cross encoder models. Embedding models use cosine similarity, and cross-encoder 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. :::

from vllm import LLM

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 here: gh-file:examples/offline_inference/basic/score.py

Online Serving

Our OpenAI-Compatible Server provides endpoints that correspond to the offline APIs:

• Pooling API is similar to LLM.encode, being applicable to all types of pooling models.
• Embeddings API is similar to LLM.embed, accepting both text and multi-modal inputs for embedding models.
• Score API is similar to LLM.score for cross-encoder models.

Matryoshka Embeddings

Matryoshka Embeddings or Matryoshka Representation Learning (MRL) is a technique used in training embedding models. It allows user to trade off between performance and cost.

:::{warning} Not all embedding models are trained using Matryoshka Representation Learning. To avoid misuse of the dimensions parameter, vLLM returns an error for requests that attempt to change the output dimension of models that do not support Matryoshka Embeddings.

For example, setting dimensions parameter while using the BAAI/bge-m3 model will result in the following error.

{"object":"error","message":"Model \"BAAI/bge-m3\" does not support matryoshka representation, changing output dimensions will lead to poor results.","type":"BadRequestError","param":null,"code":400}

:::

Manually enable Matryoshka Embeddings

There is currently no official interface for specifying support for Matryoshka Embeddings. In vLLM, we simply check the existence of the fields is_matryoshka or matryoshka_dimensions inside config.json.

For models that support Matryoshka Embeddings but not recognized by vLLM, please manually override the config using hf_overrides={"is_matryoshka": True} (offline) or --hf_overrides '{"is_matryoshka": true}' (online).

Here is an example to serve a model with Matryoshka Embeddings enabled.

vllm serve Snowflake/snowflake-arctic-embed-m-v1.5 --hf_overrides '{"is_matryoshka":true}'

Offline Inference

You can change the output dimensions of embedding models that support Matryoshka Embeddings by using the dimensions parameter in {class}~vllm.PoolingParams.

from vllm import LLM, PoolingParams

model = LLM(model="jinaai/jina-embeddings-v3", 
            task="embed", 
            trust_remote_code=True)
outputs = model.embed(["Follow the white rabbit."], 
                      pooling_params=PoolingParams(dimensions=32))
print(outputs[0].outputs)

A code example can be found here: gh-file:examples/offline_inference/embed_matryoshka_fy.py

Online Inference

Use the following command to start vllm server.

vllm serve jinaai/jina-embeddings-v3 --trust-remote-code

You can change the output dimensions of embedding models that support Matryoshka Embeddings by using the dimensions parameter.

curl http://127.0.0.1:8000/v1/embeddings \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "input": "Follow the white rabbit.",
    "model": "jinaai/jina-embeddings-v3",
    "encoding_format": "float",
    "dimensions": 1
  }'

Expected output:

{"id":"embd-0aab28c384d348c3b8f0eb783109dc5f","object":"list","created":1744195454,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-1.0]}],"usage":{"prompt_tokens":10,"total_tokens":10,"completion_tokens":0,"prompt_tokens_details":null}}

A openai client example can be found here: gh-file:examples/online_serving/openai_embedding_matryoshka_fy.py