Sync from v0.13

tests/models/language/pooling_mteb_test/mteb_utils.py

@@ -0,0 +1,415 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import tempfile
+
+import mteb
+import numpy as np
+import requests
+import torch
+from mteb.models import ModelMeta
+from mteb.types import Array
+from torch.utils.data import DataLoader
+
+import tests.ci_envs as ci_envs
+from tests.models.utils import (
+    EmbedModelInfo,
+    RerankModelInfo,
+    check_embeddings_close,
+    get_vllm_extra_kwargs,
+)
+
+# Most embedding models on the STS12 task (See #17175):
+# - Model implementation and minor changes in tensor dtype
+#   results in differences less than 1e-4
+# - Different model results in differences more than 1e-3
+# 1e-4 is a good tolerance threshold
+MTEB_EMBED_TASKS = ["STS12"]
+MTEB_EMBED_TOL = 1e-4
+
+# See #19344
+MTEB_RERANK_TASKS = ["NFCorpus"]
+MTEB_RERANK_LANGS = ["eng"]
+MTEB_RERANK_TOL = 2e-3
+
+_empty_model_meta = ModelMeta(
+    loader=None,
+    name="vllm/model",
+    revision="1",
+    release_date=None,
+    languages=None,
+    framework=[],
+    similarity_fn_name=None,
+    n_parameters=None,
+    memory_usage_mb=None,
+    max_tokens=None,
+    embed_dim=None,
+    license=None,
+    open_weights=None,
+    public_training_code=None,
+    public_training_data=None,
+    use_instructions=None,
+    training_datasets=None,
+    modalities=["text"],  # 'image' can be added to evaluate multimodal models
+)
+
+
+class VllmMtebEncoder(mteb.EncoderProtocol):
+    mteb_model_meta = _empty_model_meta
+
+    def __init__(self, vllm_model):
+        self.llm = vllm_model
+        self.rng = np.random.default_rng(seed=42)
+
+    def encode(
+        self,
+        inputs: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        # Hoping to discover potential scheduling
+        # issues by randomizing the order.
+        sentences = [text for batch in inputs for text in batch["text"]]
+        r = self.rng.permutation(len(sentences))
+        sentences = [sentences[i] for i in r]
+        outputs = self.llm.embed(sentences, use_tqdm=False)
+        embeds = np.array(outputs)
+        embeds = embeds[np.argsort(r)]
+        return embeds
+
+    def similarity(
+        self,
+        embeddings1: np.ndarray,
+        embeddings2: np.ndarray,
+    ) -> np.ndarray:
+        # Cosine similarity
+        norm1 = np.linalg.norm(embeddings1, axis=1, keepdims=True)
+        norm2 = np.linalg.norm(embeddings2, axis=1, keepdims=True)
+        sim = np.dot(embeddings1, embeddings2.T) / (norm1 * norm2.T)
+        return sim
+
+    def similarity_pairwise(
+        self,
+        embeddings1: Array,
+        embeddings2: Array,
+    ) -> Array:
+        # Cosine similarity
+        norm1 = np.linalg.norm(embeddings1, axis=1, keepdims=True)
+        norm2 = np.linalg.norm(embeddings2, axis=1, keepdims=True)
+        sim = np.sum(embeddings1 * embeddings2, axis=1) / (
+            norm1.flatten() * norm2.flatten()
+        )
+        return sim
+
+
+class VllmMtebCrossEncoder(mteb.CrossEncoderProtocol):
+    mteb_model_meta = _empty_model_meta
+
+    def __init__(self, vllm_model):
+        self.llm = vllm_model
+        self.rng = np.random.default_rng(seed=42)
+
+    def predict(
+        self,
+        inputs1: DataLoader[mteb.types.BatchedInput],
+        inputs2: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        queries = [text for batch in inputs1 for text in batch["text"]]
+        corpus = [text for batch in inputs2 for text in batch["text"]]
+
+        outputs = self.llm.score(
+            queries, corpus, truncate_prompt_tokens=-1, use_tqdm=False
+        )
+        scores = np.array(outputs)
+        return scores
+
+
+class OpenAIClientMtebEncoder(VllmMtebEncoder):
+    def __init__(self, model_name: str, client):
+        self.model_name = model_name
+        self.client = client
+        self.rng = np.random.default_rng(seed=42)
+
+    def encode(
+        self,
+        inputs: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        # Hoping to discover potential scheduling
+        # issues by randomizing the order.
+        sentences = [text for batch in inputs for text in batch["text"]]
+        r = self.rng.permutation(len(sentences))
+        sentences = [sentences[i] for i in r]
+
+        embeddings = self.client.embeddings.create(
+            model=self.model_name, input=sentences
+        )
+        outputs = [d.embedding for d in embeddings.data]
+        embeds = np.array(outputs)
+        embeds = embeds[np.argsort(r)]
+        return embeds
+
+
+class ScoreClientMtebEncoder(mteb.CrossEncoderProtocol):
+    mteb_model_meta = _empty_model_meta
+
+    def __init__(self, model_name: str, url):
+        self.model_name = model_name
+        self.url = url
+        self.rng = np.random.default_rng(seed=42)
+
+    def predict(
+        self,
+        inputs1: DataLoader[mteb.types.BatchedInput],
+        inputs2: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        queries = [text for batch in inputs1 for text in batch["text"]]
+        full_corpus = [text for batch in inputs2 for text in batch["text"]]
+
+        outputs = []
+        for query, corpus in zip(queries, full_corpus):
+            outputs.append(self.get_score(query, corpus))
+
+        scores = np.array(outputs)
+        return scores
+
+    def get_score(self, query, corpus):
+        response = requests.post(
+            self.url,
+            json={
+                "model": self.model_name,
+                "text_1": query,
+                "text_2": corpus,
+                "truncate_prompt_tokens": -1,
+            },
+        ).json()
+        return response["data"][0]["score"]
+
+
+class RerankClientMtebEncoder(ScoreClientMtebEncoder):
+    def get_score(self, query, corpus):
+        response = requests.post(
+            self.url,
+            json={
+                "model": self.model_name,
+                "query": query,
+                "documents": [corpus],
+                "truncate_prompt_tokens": -1,
+            },
+        ).json()
+        return response["results"][0]["relevance_score"]
+
+
+def run_mteb_embed_task(encoder: mteb.EncoderProtocol, tasks):
+    tasks = mteb.get_tasks(tasks=tasks)
+    results = mteb.evaluate(
+        encoder,
+        tasks,
+        cache=None,
+        show_progress_bar=False,
+    )
+
+    main_score = results[0].scores["test"][0]["main_score"]
+    return main_score
+
+
+def mteb_test_embed_models(
+    hf_runner,
+    vllm_runner,
+    model_info: EmbedModelInfo,
+    vllm_extra_kwargs=None,
+    hf_model_callback=None,
+    atol=MTEB_EMBED_TOL,
+):
+    vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
+
+    # Test embed_dims, isnan and whether to use normalize
+    example_prompts = ["The chef prepared a delicious meal." * 1000]
+
+    with vllm_runner(
+        model_info.name,
+        runner="pooling",
+        max_model_len=model_info.max_model_len,
+        **vllm_extra_kwargs,
+    ) as vllm_model:
+        model_config = vllm_model.llm.llm_engine.model_config
+
+        # Confirm whether vllm is using the correct architecture
+        if model_info.architecture:
+            assert model_info.architecture in model_config.architectures
+
+        # Confirm whether vllm uses the correct default_pooling_type, which
+        # relates to whether chunked prefill and prefix caching are enabled
+        assert (
+            model_config._model_info.default_pooling_type
+            == model_info.default_pooling_type
+        )
+
+        vllm_main_score = run_mteb_embed_task(
+            VllmMtebEncoder(vllm_model), MTEB_EMBED_TASKS
+        )
+        vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
+        head_dtype = model_config.head_dtype
+
+        # Test embedding_size, isnan and whether to use normalize
+        vllm_outputs = vllm_model.embed(example_prompts, truncate_prompt_tokens=-1)
+        outputs_tensor = torch.tensor(vllm_outputs)
+        assert not torch.any(torch.isnan(outputs_tensor))
+        embedding_size = model_config.embedding_size
+        assert torch.tensor(vllm_outputs).shape[-1] == embedding_size
+
+    # Accelerate mteb test by setting
+    # SentenceTransformers mteb score to a constant
+    if model_info.mteb_score is None:
+        with hf_runner(
+            model_info.name,
+            is_sentence_transformer=True,
+            dtype=ci_envs.VLLM_CI_HF_DTYPE or model_info.hf_dtype,
+        ) as hf_model:
+            # e.g. setting default parameters for the encode method of hf_runner
+            if hf_model_callback is not None:
+                hf_model_callback(hf_model)
+
+            st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
+            st_dtype = next(hf_model.model.parameters()).dtype
+
+            # Check embeddings close to hf outputs
+            hf_outputs = hf_model.encode(example_prompts)
+            check_embeddings_close(
+                embeddings_0_lst=hf_outputs,
+                embeddings_1_lst=vllm_outputs,
+                name_0="hf",
+                name_1="vllm",
+                tol=1e-2,
+            )
+    else:
+        st_main_score = model_info.mteb_score
+        st_dtype = "Constant"
+
+    print("Model:", model_info.name)
+    print("VLLM:", f"dtype:{vllm_dtype}", f"head_dtype:{head_dtype}", vllm_main_score)
+    print("SentenceTransformers:", st_dtype, st_main_score)
+    print("Difference:", st_main_score - vllm_main_score)
+
+    # We are not concerned that the vllm mteb results are better
+    # than SentenceTransformers, so we only perform one-sided testing.
+    assert st_main_score - vllm_main_score < atol
+
+
+def run_mteb_rerank(cross_encoder: mteb.CrossEncoderProtocol, tasks, languages):
+    with tempfile.TemporaryDirectory() as prediction_folder:
+        bm25s = mteb.get_model("bm25s")
+        eval_splits = ["test"]
+
+        mteb_tasks: list[mteb.abstasks.AbsTaskRetrieval] = mteb.get_tasks(
+            tasks=tasks, languages=languages, eval_splits=eval_splits
+        )
+
+        mteb.evaluate(
+            bm25s,
+            mteb_tasks,
+            prediction_folder=prediction_folder,
+            show_progress_bar=False,
+            # don't save results for test runs
+            cache=None,
+            overwrite_strategy="always",
+        )
+
+        second_stage_tasks = []
+        for task in mteb_tasks:
+            second_stage_tasks.append(
+                task.convert_to_reranking(
+                    prediction_folder,
+                    top_k=10,
+                )
+            )
+
+        results = mteb.evaluate(
+            cross_encoder,
+            second_stage_tasks,
+            show_progress_bar=False,
+            cache=None,
+        )
+        main_score = results[0].scores["test"][0]["main_score"]
+    return main_score
+
+
+def mteb_test_rerank_models_hf(
+    hf_runner, model_name, hf_dtype="float32", hf_model_callback=None
+):
+    with hf_runner(model_name, is_cross_encoder=True, dtype=hf_dtype) as hf_model:
+        if hf_model_callback is not None:
+            hf_model_callback(hf_model)
+
+        st_main_score = run_mteb_rerank(
+            hf_model, tasks=MTEB_RERANK_TASKS, languages=MTEB_RERANK_LANGS
+        )
+        st_dtype = next(hf_model.model.model.parameters()).dtype
+    return st_main_score, st_dtype
+
+
+def mteb_test_rerank_models(
+    hf_runner,
+    vllm_runner,
+    model_info: RerankModelInfo,
+    vllm_extra_kwargs=None,
+    hf_model_callback=None,
+    vllm_mteb_encoder=VllmMtebCrossEncoder,
+    atol=MTEB_RERANK_TOL,
+):
+    vllm_extra_kwargs = get_vllm_extra_kwargs(model_info, vllm_extra_kwargs)
+
+    with vllm_runner(
+        model_info.name,
+        runner="pooling",
+        max_model_len=None,
+        max_num_seqs=8,
+        **vllm_extra_kwargs,
+    ) as vllm_model:
+        model_config = vllm_model.llm.llm_engine.model_config
+
+        # Confirm whether vllm is using the correct architecture
+        if model_info.architecture:
+            assert model_info.architecture in model_config.architectures
+
+        # Score API is only enabled for num_labels == 1
+        assert model_config.hf_config.num_labels == 1
+
+        # Confirm whether vllm uses the correct default_pooling_type, which
+        # relates to whether chunked prefill and prefix caching are enabled
+        assert (
+            model_config._model_info.default_pooling_type
+            == model_info.default_pooling_type
+        )
+
+        vllm_main_score = run_mteb_rerank(
+            vllm_mteb_encoder(vllm_model),
+            tasks=MTEB_RERANK_TASKS,
+            languages=MTEB_RERANK_LANGS,
+        )
+        vllm_dtype = model_config.dtype
+        head_dtype = model_config.head_dtype
+
+    # Accelerate mteb test by setting
+    # SentenceTransformers mteb score to a constant
+    if model_info.mteb_score is None:
+        st_main_score, st_dtype = mteb_test_rerank_models_hf(
+            hf_runner, model_info.name, model_info.hf_dtype, hf_model_callback
+        )
+    else:
+        st_main_score = model_info.mteb_score
+        st_dtype = "Constant"
+
+    print("Model:", model_info.name)
+    print("VLLM:", f"dtype:{vllm_dtype}", f"head_dtype:{head_dtype}", vllm_main_score)
+    print("SentenceTransformers:", st_dtype, st_main_score)
+    print("Difference:", st_main_score - vllm_main_score)
+
+    # We are not concerned that the vllm mteb results are better
+    # than SentenceTransformers, so we only perform one-sided testing.
+    assert st_main_score - vllm_main_score < atol

tests/models/language/pooling_mteb_test/test_baai.py

@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from tests.models.language.pooling.embed_utils import correctness_test_embed_models
+from tests.models.utils import (
+    CLSPoolingEmbedModelInfo,
+    CLSPoolingRerankModelInfo,
+    EmbedModelInfo,
+    LASTPoolingEmbedModelInfo,
+    RerankModelInfo,
+)
+
+from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
+
+MODELS = [
+    ########## BertModel
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-base-en",
+        architecture="BertModel",
+        mteb_score=0.779336792,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-base-zh", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-small-en", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-small-zh", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-large-en", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-large-zh", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-large-zh-noinstruct", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-base-en-v1.5", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-base-zh-v1.5", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-small-en-v1.5", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-small-zh-v1.5", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-large-en-v1.5", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-large-zh-v1.5", architecture="BertModel", enable_test=False
+    ),
+    ########## XLMRobertaModel
+    CLSPoolingEmbedModelInfo(
+        "BAAI/bge-m3",
+        architecture="XLMRobertaModel",
+        mteb_score=0.787343078,
+        enable_test=True,
+    ),
+    ########## Qwen2Model
+    LASTPoolingEmbedModelInfo(
+        "BAAI/bge-code-v1",
+        architecture="Qwen2Model",
+        mteb_score=0.75724465,
+        dtype="float32",
+        enable_test=True,
+    ),
+]
+
+RERANK_MODELS = [
+    ########## XLMRobertaForSequenceClassification
+    CLSPoolingRerankModelInfo(
+        "BAAI/bge-reranker-base",
+        architecture="XLMRobertaForSequenceClassification",
+        mteb_score=0.32398,
+        enable_test=True,
+    ),
+    CLSPoolingRerankModelInfo(
+        "BAAI/bge-reranker-large",
+        architecture="XLMRobertaForSequenceClassification",
+        enable_test=False,
+    ),
+    CLSPoolingRerankModelInfo(
+        "BAAI/bge-reranker-v2-m3",
+        architecture="XLMRobertaForSequenceClassification",
+        enable_test=False,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(
+    hf_runner, vllm_runner, model_info: RerankModelInfo
+) -> None:
+    mteb_test_rerank_models(hf_runner, vllm_runner, model_info)

tests/models/language/pooling_mteb_test/test_bge_reranker_v2_gemma.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import mteb
+import numpy as np
+import pytest
+import torch
+from torch.utils.data import DataLoader
+
+from tests.conftest import HfRunner
+from tests.models.language.pooling_mteb_test.mteb_utils import (
+    VllmMtebCrossEncoder,
+    mteb_test_rerank_models,
+)
+from tests.models.utils import LASTPoolingRerankModelInfo, RerankModelInfo
+
+RERANK_MODELS = [
+    LASTPoolingRerankModelInfo(
+        "BAAI/bge-reranker-v2-gemma",
+        architecture="GemmaForSequenceClassification",
+        mteb_score=0.33757,
+        hf_overrides={
+            "architectures": ["GemmaForSequenceClassification"],
+            "classifier_from_token": ["Yes"],
+            "method": "no_post_processing",
+        },
+    ),
+]
+
+PROMPT = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."  # noqa: E501
+
+
+class GemmaRerankerHfRunner(HfRunner):
+    def __init__(
+        self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
+    ) -> None:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        self.yes_loc = self.tokenizer.convert_tokens_to_ids("Yes")
+
+    @torch.no_grad()
+    def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
+        def get_inputs(pairs, tokenizer, prompt=None):
+            if prompt is None:
+                prompt = PROMPT
+
+            sep = "\n"
+            prompt_inputs = tokenizer(
+                prompt, return_tensors=None, add_special_tokens=False
+            )["input_ids"]
+            sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)[
+                "input_ids"
+            ]
+            inputs = []
+            for query, passage in pairs:
+                query_inputs = tokenizer(
+                    f"A: {query}",
+                    return_tensors=None,
+                    add_special_tokens=False,
+                    truncation=True,
+                )
+                passage_inputs = tokenizer(
+                    f"B: {passage}",
+                    return_tensors=None,
+                    add_special_tokens=False,
+                    truncation=True,
+                )
+                item = tokenizer.prepare_for_model(
+                    [tokenizer.bos_token_id] + query_inputs["input_ids"],
+                    sep_inputs + passage_inputs["input_ids"],
+                    truncation="only_second",
+                    padding=False,
+                    return_attention_mask=False,
+                    return_token_type_ids=False,
+                    add_special_tokens=False,
+                )
+                item["input_ids"] = item["input_ids"] + sep_inputs + prompt_inputs
+                item["attention_mask"] = [1] * len(item["input_ids"])
+                inputs.append(item)
+            return tokenizer.pad(
+                inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+
+        scores = []
+        for query, doc, *_ in prompts:
+            pairs = [(query, doc)]
+            inputs = get_inputs(pairs, self.tokenizer)
+            inputs = inputs.to(self.model.device)
+            _n_tokens = inputs["input_ids"].shape[1]
+            logits = self.model(**inputs, return_dict=True).logits
+            _scores = (
+                logits[:, -1, self.yes_loc]
+                .view(
+                    -1,
+                )
+                .float()
+                .sigmoid()
+            )
+            scores.append(_scores[0].item())
+        return torch.Tensor(scores)
+
+
+class GemmaMtebEncoder(VllmMtebCrossEncoder):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.query_template = "A: {query}\n"
+        self.document_template = "B: {doc}\n{prompt}"
+
+    def predict(
+        self,
+        inputs1: DataLoader[mteb.types.BatchedInput],
+        inputs2: DataLoader[mteb.types.BatchedInput],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        queries = [
+            self.query_template.format(query=text)
+            for batch in inputs1
+            for text in batch["text"]
+        ]
+        corpus = [
+            self.document_template.format(doc=text, prompt=PROMPT)
+            for batch in inputs2
+            for text in batch["text"]
+        ]
+        outputs = self.llm.score(
+            queries, corpus, truncate_prompt_tokens=-1, use_tqdm=False
+        )
+        scores = np.array(outputs)
+        return scores
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
+    mteb_test_rerank_models(
+        GemmaRerankerHfRunner,
+        vllm_runner,
+        model_info,
+        vllm_mteb_encoder=GemmaMtebEncoder,
+    )

tests/models/language/pooling_mteb_test/test_cross_encoder.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from tests.models.utils import (
+    CLSPoolingRerankModelInfo,
+    LASTPoolingRerankModelInfo,
+    RerankModelInfo,
+)
+
+from .mteb_utils import mteb_test_rerank_models
+
+RERANK_MODELS = [
+    CLSPoolingRerankModelInfo(
+        "cross-encoder/ms-marco-TinyBERT-L-2-v2",
+        mteb_score=0.32898,
+        architecture="BertForSequenceClassification",
+    ),
+    LASTPoolingRerankModelInfo(
+        "tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
+        mteb_score=0.25736,
+        architecture="Qwen3ForSequenceClassification",
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(
+    hf_runner, vllm_runner, model_info: RerankModelInfo
+) -> None:
+    mteb_test_rerank_models(hf_runner, vllm_runner, model_info)

tests/models/language/pooling_mteb_test/test_gte.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from tests.models.language.pooling.embed_utils import correctness_test_embed_models
+from tests.models.utils import (
+    CLSPoolingEmbedModelInfo,
+    CLSPoolingRerankModelInfo,
+    EmbedModelInfo,
+    LASTPoolingEmbedModelInfo,
+    RerankModelInfo,
+)
+
+from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
+
+MODELS = [
+    ########## BertModel
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-large",
+        mteb_score=0.76807651,
+        architecture="BertModel",
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-base", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-small", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-large-zh", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-base-zh", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "thenlper/gte-small-zh", architecture="BertModel", enable_test=False
+    ),
+    ########### NewModel
+    # These three architectures are almost the same, but not exactly the same.
+    # For example,
+    # - whether to use token_type_embeddings
+    # - whether to use context expansion
+    # So only test one (the most widely used) model
+    CLSPoolingEmbedModelInfo(
+        "Alibaba-NLP/gte-multilingual-base",
+        architecture="GteNewModel",
+        mteb_score=0.775074696,
+        hf_overrides={"architectures": ["GteNewModel"]},
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Alibaba-NLP/gte-base-en-v1.5",
+        architecture="GteNewModel",
+        hf_overrides={"architectures": ["GteNewModel"]},
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Alibaba-NLP/gte-large-en-v1.5",
+        architecture="GteNewModel",
+        hf_overrides={"architectures": ["GteNewModel"]},
+        enable_test=False,
+    ),
+    ########### Qwen2ForCausalLM
+    LASTPoolingEmbedModelInfo(
+        "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+        mteb_score=0.758473459018872,
+        architecture="Qwen2ForCausalLM",
+        enable_test=True,
+    ),
+    ########## ModernBertModel
+    CLSPoolingEmbedModelInfo(
+        "Alibaba-NLP/gte-modernbert-base",
+        mteb_score=0.748193353,
+        architecture="ModernBertModel",
+        enable_test=True,
+    ),
+    ########## Qwen3ForCausalLM
+    LASTPoolingEmbedModelInfo(
+        "Qwen/Qwen3-Embedding-0.6B",
+        mteb_score=0.771163695,
+        architecture="Qwen3ForCausalLM",
+        dtype="float32",
+        enable_test=True,
+    ),
+    LASTPoolingEmbedModelInfo(
+        "Qwen/Qwen3-Embedding-4B",
+        architecture="Qwen3ForCausalLM",
+        dtype="float32",
+        enable_test=False,
+    ),
+]
+
+RERANK_MODELS = [
+    CLSPoolingRerankModelInfo(
+        # classifier_pooling: mean
+        "Alibaba-NLP/gte-reranker-modernbert-base",
+        mteb_score=0.33386,
+        architecture="ModernBertForSequenceClassification",
+        enable_test=True,
+    ),
+    CLSPoolingRerankModelInfo(
+        "Alibaba-NLP/gte-multilingual-reranker-base",
+        mteb_score=0.33062,
+        architecture="GteNewForSequenceClassification",
+        hf_overrides={"architectures": ["GteNewForSequenceClassification"]},
+        enable_test=True,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(
+    hf_runner, vllm_runner, model_info: RerankModelInfo
+) -> None:
+    mteb_test_rerank_models(hf_runner, vllm_runner, model_info)

tests/models/language/pooling_mteb_test/test_intfloat.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from tests.models.language.pooling.embed_utils import correctness_test_embed_models
+from tests.models.utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
+
+from .mteb_utils import mteb_test_embed_models
+
+MODELS = [
+    ########## BertModel
+    CLSPoolingEmbedModelInfo(
+        "intfloat/e5-small",
+        architecture="BertModel",
+        mteb_score=0.742285423,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "intfloat/e5-base", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "intfloat/e5-large", architecture="BertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "intfloat/multilingual-e5-small", architecture="BertModel", enable_test=False
+    ),
+    ########## XLMRobertaModel
+    CLSPoolingEmbedModelInfo(
+        "intfloat/multilingual-e5-base",
+        architecture="XLMRobertaModel",
+        mteb_score=0.779325955,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "intfloat/multilingual-e5-large",
+        architecture="XLMRobertaModel",
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "intfloat/multilingual-e5-large-instruct",
+        architecture="XLMRobertaModel",
+        enable_test=False,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)

tests/models/language/pooling_mteb_test/test_jina.py

@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from functools import partial
+
+import pytest
+
+from tests.models.language.pooling.embed_utils import (
+    check_embeddings_close,
+    correctness_test_embed_models,
+    matryoshka_fy,
+)
+from tests.models.utils import (
+    CLSPoolingEmbedModelInfo,
+    CLSPoolingRerankModelInfo,
+    EmbedModelInfo,
+    RerankModelInfo,
+)
+from vllm import PoolingParams
+
+from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
+
+EMBEDDING_MODELS = [
+    CLSPoolingEmbedModelInfo(
+        "jinaai/jina-embeddings-v3",
+        mteb_score=0.824413164,
+        architecture="XLMRobertaModel",
+        is_matryoshka=True,
+        dtype="float32",
+    )
+]
+
+RERANK_MODELS = [
+    CLSPoolingRerankModelInfo(
+        "jinaai/jina-reranker-v2-base-multilingual",
+        mteb_score=0.33643,
+        architecture="XLMRobertaForSequenceClassification",
+    )
+]
+
+
+@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    def hf_model_callback(model):
+        model.encode = partial(model.encode, task="text-matching")
+
+    mteb_test_embed_models(
+        hf_runner, vllm_runner, model_info, hf_model_callback=hf_model_callback
+    )
+
+
+@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    def hf_model_callback(model):
+        model.encode = partial(model.encode, task="text-matching")
+
+    correctness_test_embed_models(
+        hf_runner,
+        vllm_runner,
+        model_info,
+        example_prompts,
+        hf_model_callback=hf_model_callback,
+    )
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(
+    hf_runner, vllm_runner, model_info: RerankModelInfo
+) -> None:
+    mteb_test_rerank_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", EMBEDDING_MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("dimensions", [16, 32])
+def test_matryoshka(
+    hf_runner,
+    vllm_runner,
+    model_info,
+    dtype: str,
+    dimensions: int,
+    example_prompts,
+    monkeypatch,
+) -> None:
+    if not model_info.is_matryoshka:
+        pytest.skip("Model is not matryoshka")
+
+    # ST will strip the input texts, see test_embedding.py
+    example_prompts = [str(s).strip() for s in example_prompts]
+
+    with hf_runner(
+        model_info.name,
+        dtype=dtype,
+        is_sentence_transformer=True,
+    ) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
+        hf_outputs = matryoshka_fy(hf_outputs, dimensions)
+
+    with vllm_runner(
+        model_info.name, runner="pooling", dtype=dtype, max_model_len=None
+    ) as vllm_model:
+        assert vllm_model.llm.llm_engine.model_config.is_matryoshka
+
+        matryoshka_dimensions = (
+            vllm_model.llm.llm_engine.model_config.matryoshka_dimensions
+        )
+        assert matryoshka_dimensions is not None
+
+        if dimensions not in matryoshka_dimensions:
+            with pytest.raises(ValueError):
+                vllm_model.embed(
+                    example_prompts, pooling_params=PoolingParams(dimensions=dimensions)
+                )
+        else:
+            vllm_outputs = vllm_model.embed(
+                example_prompts, pooling_params=PoolingParams(dimensions=dimensions)
+            )
+
+            check_embeddings_close(
+                embeddings_0_lst=hf_outputs,
+                embeddings_1_lst=vllm_outputs,
+                name_0="hf",
+                name_1="vllm",
+                tol=1e-2,
+            )

tests/models/language/pooling_mteb_test/test_mxbai_rerank.py

@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import pytest
+import torch
+
+from tests.conftest import HfRunner
+from tests.models.utils import LASTPoolingRerankModelInfo, RerankModelInfo
+
+from .mteb_utils import mteb_test_rerank_models
+
+mxbai_rerank_hf_overrides = {
+    "architectures": ["Qwen2ForSequenceClassification"],
+    "classifier_from_token": ["0", "1"],
+    "method": "from_2_way_softmax",
+}
+
+RERANK_MODELS = [
+    LASTPoolingRerankModelInfo(
+        "mixedbread-ai/mxbai-rerank-base-v2",
+        architecture="Qwen2ForSequenceClassification",
+        hf_overrides=mxbai_rerank_hf_overrides,
+        mteb_score=0.273,
+        enable_test=True,
+    ),
+    LASTPoolingRerankModelInfo(
+        "mixedbread-ai/mxbai-rerank-large-v2",
+        architecture="Qwen2ForSequenceClassification",
+        hf_overrides=mxbai_rerank_hf_overrides,
+        enable_test=False,
+    ),
+]
+
+
+class MxbaiRerankerHfRunner(HfRunner):
+    def __init__(
+        self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
+    ) -> None:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        self.yes_loc = self.tokenizer.convert_tokens_to_ids("1")
+        self.no_loc = self.tokenizer.convert_tokens_to_ids("0")
+
+    def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
+        def process_inputs(pairs):
+            inputs = self.tokenizer(
+                pairs,
+                padding=False,
+                truncation="longest_first",
+                return_attention_mask=False,
+            )
+            for i, ele in enumerate(inputs["input_ids"]):
+                inputs["input_ids"][i] = ele
+            inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt")
+            for key in inputs:
+                inputs[key] = inputs[key].to(self.model.device)
+            return inputs
+
+        @torch.no_grad()
+        def compute_logits(inputs):
+            logits = self.model(**inputs).logits[:, -1, :]
+            yes_logits = logits[:, self.yes_loc]
+            no_logits = logits[:, self.no_loc]
+            logits = yes_logits - no_logits
+            scores = logits.float().sigmoid()
+            return scores
+
+        scores = []
+        for query, doc, *_ in prompts:
+            pairs = [(query, doc)]
+            inputs = process_inputs(pairs)
+            score = compute_logits(inputs)
+            scores.append(score[0].item())
+        return torch.Tensor(scores)
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
+    mteb_test_rerank_models(MxbaiRerankerHfRunner, vllm_runner, model_info)

tests/models/language/pooling_mteb_test/test_nomic.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from tests.models.language.pooling.embed_utils import correctness_test_embed_models
+from tests.models.utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
+
+from .mteb_utils import mteb_test_embed_models
+
+MODELS = [
+    CLSPoolingEmbedModelInfo(
+        "nomic-ai/nomic-embed-text-v1",
+        architecture="NomicBertModel",
+        mteb_score=0.737568559,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "nomic-ai/nomic-embed-text-v1.5",
+        architecture="NomicBertModel",
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "nomic-ai/CodeRankEmbed", architecture="NomicBertModel", enable_test=False
+    ),
+    CLSPoolingEmbedModelInfo(
+        "nomic-ai/nomic-embed-text-v2-moe",
+        architecture="NomicBertModel",
+        mteb_score=0.715488912,
+        enable_test=True,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)

tests/models/language/pooling_mteb_test/test_qwen3_reranker.py

@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import pytest
+import torch
+
+from tests.conftest import HfRunner
+from tests.models.utils import LASTPoolingRerankModelInfo, RerankModelInfo
+from tests.utils import multi_gpu_test
+
+from .mteb_utils import mteb_test_rerank_models
+
+qwen3_reranker_hf_overrides = {
+    "architectures": ["Qwen3ForSequenceClassification"],
+    "classifier_from_token": ["no", "yes"],
+    "is_original_qwen3_reranker": True,
+}
+
+RERANK_MODELS = [
+    LASTPoolingRerankModelInfo(
+        "Qwen/Qwen3-Reranker-0.6B",
+        architecture="Qwen3ForSequenceClassification",
+        mteb_score=0.25736,
+        hf_overrides=qwen3_reranker_hf_overrides,
+        enable_test=True,
+    ),
+    LASTPoolingRerankModelInfo(
+        "Qwen/Qwen3-Reranker-4B",
+        architecture="Qwen3ForSequenceClassification",
+        hf_overrides=qwen3_reranker_hf_overrides,
+        enable_test=False,
+    ),
+]
+
+
+class Qwen3RerankerHfRunner(HfRunner):
+    def __init__(
+        self, model_name: str, dtype: str = "auto", *args: Any, **kwargs: Any
+    ) -> None:
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        self.token_false_id = self.tokenizer.convert_tokens_to_ids("no")
+        self.token_true_id = self.tokenizer.convert_tokens_to_ids("yes")
+
+    def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor:
+        def process_inputs(pairs):
+            inputs = self.tokenizer(
+                pairs,
+                padding=False,
+                truncation="longest_first",
+                return_attention_mask=False,
+            )
+            for i, ele in enumerate(inputs["input_ids"]):
+                inputs["input_ids"][i] = ele
+            inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt")
+            for key in inputs:
+                inputs[key] = inputs[key].to(self.model.device)
+            return inputs
+
+        @torch.no_grad()
+        def compute_logits(inputs):
+            batch_scores = self.model(**inputs).logits[:, -1, :]
+            true_vector = batch_scores[:, self.token_true_id]
+            false_vector = batch_scores[:, self.token_false_id]
+            batch_scores = torch.stack([false_vector, true_vector], dim=1)
+            batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
+            scores = batch_scores[:, 1].exp()
+            return scores
+
+        scores = []
+        for query, doc, *_ in prompts:
+            pairs = [(query, doc)]
+            inputs = process_inputs(pairs)
+            score = compute_logits(inputs)
+            scores.append(score[0].item())
+        return torch.Tensor(scores)
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
+    mteb_test_rerank_models(Qwen3RerankerHfRunner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", RERANK_MODELS)
+@multi_gpu_test(num_gpus=2)
+def test_rerank_models_mteb_tp(vllm_runner, model_info: RerankModelInfo) -> None:
+    assert model_info.architecture == "Qwen3ForSequenceClassification"
+
+    vllm_extra_kwargs: dict[str, Any] = {
+        "tensor_parallel_size": 2,
+    }
+
+    mteb_test_rerank_models(
+        Qwen3RerankerHfRunner, vllm_runner, model_info, vllm_extra_kwargs
+    )

tests/models/language/pooling_mteb_test/test_snowflake_arctic_embed.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from tests.models.language.pooling.embed_utils import correctness_test_embed_models
+from tests.models.utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
+
+from .mteb_utils import mteb_test_embed_models
+
+MODELS = [
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-xs",
+        is_matryoshka=False,
+        architecture="BertModel",
+        mteb_score=0.714927797,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-s",
+        is_matryoshka=False,
+        architecture="BertModel",
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-m",
+        is_matryoshka=False,
+        architecture="BertModel",
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-m-long",
+        is_matryoshka=False,
+        architecture="NomicBertModel",
+        mteb_score=0.681146831,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-l",
+        is_matryoshka=False,
+        architecture="BertModel",
+        enable_test=False,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-m-v1.5",
+        is_matryoshka=True,
+        architecture="BertModel",
+        mteb_score=0.649088363,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-l-v2.0",
+        is_matryoshka=True,
+        architecture="XLMRobertaModel",
+        mteb_score=0.712258299,
+        enable_test=True,
+    ),
+    CLSPoolingEmbedModelInfo(
+        "Snowflake/snowflake-arctic-embed-m-v2.0",
+        is_matryoshka=True,
+        architecture="GteModel",
+        mteb_score=0.706622444,
+        enable_test=True,
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(
+    hf_runner, vllm_runner, model_info: EmbedModelInfo, example_prompts
+) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info, example_prompts)

tests/models/language/pooling_mteb_test/test_st_projector.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from tests.models.utils import (
+    CLSPoolingEmbedModelInfo,
+    EmbedModelInfo,
+    LASTPoolingEmbedModelInfo,
+)
+
+from .mteb_utils import mteb_test_embed_models
+
+# ST models with projector (Dense) layers
+ST_PROJECTOR_MODELS = [
+    CLSPoolingEmbedModelInfo(
+        "TencentBAC/Conan-embedding-v1",
+        architecture="BertModel",
+        mteb_score=0.688611955,
+        enable_test=True,
+    ),
+    LASTPoolingEmbedModelInfo(
+        "google/embeddinggemma-300m",
+        architecture="Gemma3TextModel",
+        mteb_score=0.7473819294684156,
+        enable_test=True,
+        dtype="float32",
+    ),
+]
+
+
+@pytest.mark.parametrize("model_info", ST_PROJECTOR_MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner, model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)