[New Model]: Support GteNewModelForSequenceClassification (#23524)

Signed-off-by: wang.yuqi <noooop@126.com>

docs/models/supported_models.md

@@ -497,6 +497,7 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
 |--------------|--------|-------------------|----------------------|---------------------------|---------------------|
 | `BertForSequenceClassification` | BERT-based | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc. | | | ✅︎ |
 | `GemmaForSequenceClassification` | Gemma-based | `BAAI/bge-reranker-v2-gemma` (see note), etc. | ✅︎ | ✅︎ | ✅︎ |
+| `GteNewForSequenceClassification` | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-reranker-base`, etc. |  |  | ✅︎ |
 | `Qwen2ForSequenceClassification` | Qwen2-based | `mixedbread-ai/mxbai-rerank-base-v2` (see note), etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3ForSequenceClassification` | Qwen3-based | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | ✅︎ | ✅︎ | ✅︎ |
 | `RobertaForSequenceClassification` | RoBERTa-based | `cross-encoder/quora-roberta-base`, etc. | | | ✅︎ |
@@ -513,6 +514,9 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
     vllm serve BAAI/bge-reranker-v2-gemma --hf_overrides '{"architectures": ["GemmaForSequenceClassification"],"classifier_from_token": ["Yes"],"method": "no_post_processing"}'
     ```
 
+!!! note
+    The second-generation GTE model (mGTE-TRM) is named `NewForSequenceClassification`. The name `NewForSequenceClassification` is too generic, you should set `--hf-overrides '{"architectures": ["GteNewForSequenceClassification"]}'` to specify the use of the `GteNewForSequenceClassification` architecture.
+
 !!! note
     Load the official original `mxbai-rerank-v2` by using the following command.
 

tests/conftest.py

@@ -456,11 +456,10 @@ class HfRunner:
         # output is final logits
         all_inputs = self.get_inputs(prompts)
         outputs = []
+        problem_type = getattr(self.config, "problem_type", "")
+
         for inputs in all_inputs:
             output = self.model(**self.wrap_device(inputs))
-
-            problem_type = getattr(self.config, "problem_type", "")
-
             if problem_type == "regression":
                 logits = output.logits[0].tolist()
             elif problem_type == "multi_label_classification":

tests/models/language/pooling/embed_utils.py

@@ -51,6 +51,9 @@ def correctness_test_embed_models(hf_runner,
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
+    if model_info.hf_overrides is not None:
+        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
+
     with vllm_runner(model_info.name,
                      runner="pooling",
                      max_model_len=None,

tests/models/language/pooling/mteb_utils.py

@@ -172,6 +172,9 @@ def mteb_test_embed_models(hf_runner,
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
+    if model_info.hf_overrides is not None:
+        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
+
     with vllm_runner(model_info.name,
                      runner="pooling",
                      max_model_len=None,
@@ -284,6 +287,9 @@ def mteb_test_rerank_models(hf_runner,
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
+    if model_info.hf_overrides is not None:
+        vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
+
     with vllm_runner(model_info.name,
                      runner="pooling",
                      max_model_len=None,

tests/models/language/pooling/test_bge_reranker_v2_gemma.py

@@ -13,7 +13,14 @@ from .mteb_utils import VllmMtebEncoder, mteb_test_rerank_models
 
 RERANK_MODELS = [
     LASTPoolingRerankModelInfo("BAAI/bge-reranker-v2-gemma",
-                               architecture="GemmaForSequenceClassification"),
+                               architecture="GemmaForSequenceClassification",
+                               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
@@ -119,22 +126,9 @@ class GemmaMtebEncoder(VllmMtebEncoder):
 
 
 @pytest.mark.parametrize("model_info", RERANK_MODELS)
-def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo,
-                            monkeypatch) -> None:
-    monkeypatch.setenv("VLLM_USE_V1", "0")
-
-    assert model_info.architecture == "GemmaForSequenceClassification"
-
-    vllm_extra_kwargs: dict[str, Any] = {
-        "hf_overrides": {
-            "architectures": ["GemmaForSequenceClassification"],
-            "classifier_from_token": ["Yes"],
-            "method": "no_post_processing",
-        }
-    }
+def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
 
     mteb_test_rerank_models(GemmaRerankerHfRunner,
                             vllm_runner,
                             model_info,
-                            vllm_extra_kwargs,
                             vllm_mteb_encoder=GemmaMtebEncoder)

tests/models/language/pooling/test_gte.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any
 
 import pytest
 
@@ -33,12 +32,15 @@ MODELS = [
     ########### NewModel
     CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
                              architecture="GteNewModel",
+                             hf_overrides={"architectures": ["GteNewModel"]},
                              enable_test=True),
     CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
                              architecture="GteNewModel",
+                             hf_overrides={"architectures": ["GteNewModel"]},
                              enable_test=True),
     CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
                              architecture="GteNewModel",
+                             hf_overrides={"architectures": ["GteNewModel"]},
                              enable_test=True),
     ########### Qwen2ForCausalLM
     LASTPoolingEmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
@@ -60,11 +62,16 @@ MODELS = [
 ]
 
 RERANK_MODELS = [
-    # classifier_pooling: mean
     CLSPoolingRerankModelInfo(
+        # classifier_pooling: mean
         "Alibaba-NLP/gte-reranker-modernbert-base",
         architecture="ModernBertForSequenceClassification",
         enable_test=True),
+    CLSPoolingRerankModelInfo(
+        "Alibaba-NLP/gte-multilingual-reranker-base",
+        architecture="GteNewForSequenceClassification",
+        hf_overrides={"architectures": ["GteNewForSequenceClassification"]},
+        enable_test=True),
 ]
 
 
@@ -75,12 +82,7 @@ def test_embed_models_mteb(hf_runner, vllm_runner,
         check_transformers_version(model_info.name,
                                    max_transformers_version="4.53.2")
 
-    vllm_extra_kwargs: dict[str, Any] = {}
-    if model_info.architecture == "GteNewModel":
-        vllm_extra_kwargs["hf_overrides"] = {"architectures": ["GteNewModel"]}
-
-    mteb_test_embed_models(hf_runner, vllm_runner, model_info,
-                           vllm_extra_kwargs)
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
 
 
 @pytest.mark.parametrize("model_info", MODELS)
@@ -91,12 +93,8 @@ def test_embed_models_correctness(hf_runner, vllm_runner,
         check_transformers_version(model_info.name,
                                    max_transformers_version="4.53.2")
 
-    vllm_extra_kwargs: dict[str, Any] = {}
-    if model_info.architecture == "GteNewModel":
-        vllm_extra_kwargs["hf_overrides"] = {"architectures": ["GteNewModel"]}
-
     correctness_test_embed_models(hf_runner, vllm_runner, model_info,
-                                  example_prompts, vllm_extra_kwargs)
+                                  example_prompts)
 
 
 @pytest.mark.parametrize("model_info", RERANK_MODELS)

tests/models/language/pooling/test_mxbai_rerank.py

@@ -10,12 +10,20 @@ from tests.conftest import HfRunner
 from ...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,
                                enable_test=True),
     LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
                                architecture="Qwen2ForSequenceClassification",
+                               hf_overrides=mxbai_rerank_hf_overrides,
                                enable_test=False)
 ]
 
@@ -71,13 +79,4 @@ class MxbaiRerankerHfRunner(HfRunner):
 
 @pytest.mark.parametrize("model_info", RERANK_MODELS)
 def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
-    vllm_extra_kwargs: dict[str, Any] = {}
-    if model_info.architecture == "Qwen2ForSequenceClassification":
-        vllm_extra_kwargs["hf_overrides"] = {
-            "architectures": ["Qwen2ForSequenceClassification"],
-            "classifier_from_token": ["0", "1"],
-            "method": "from_2_way_softmax",
-        }
-
-    mteb_test_rerank_models(MxbaiRerankerHfRunner, vllm_runner, model_info,
-                            vllm_extra_kwargs)
+    mteb_test_rerank_models(MxbaiRerankerHfRunner, vllm_runner, model_info)

tests/models/language/pooling/test_qwen3_reranker.py

@@ -11,12 +11,20 @@ from tests.utils import multi_gpu_test
 from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
 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",
+                               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)
 ]
 
@@ -74,18 +82,7 @@ class Qwen3RerankerHfRunner(HfRunner):
 @pytest.mark.parametrize("model_info", RERANK_MODELS)
 def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
 
-    assert model_info.architecture == "Qwen3ForSequenceClassification"
-
-    vllm_extra_kwargs: dict[str, Any] = {
-        "hf_overrides": {
-            "architectures": ["Qwen3ForSequenceClassification"],
-            "classifier_from_token": ["no", "yes"],
-            "is_original_qwen3_reranker": True,
-        }
-    }
-
-    mteb_test_rerank_models(Qwen3RerankerHfRunner, vllm_runner, model_info,
-                            vllm_extra_kwargs)
+    mteb_test_rerank_models(Qwen3RerankerHfRunner, vllm_runner, model_info)
 
 
 @pytest.mark.parametrize("model_info", RERANK_MODELS)
@@ -96,11 +93,6 @@ def test_rerank_models_mteb_tp(vllm_runner,
     assert model_info.architecture == "Qwen3ForSequenceClassification"
 
     vllm_extra_kwargs: dict[str, Any] = {
-        "hf_overrides": {
-            "architectures": ["Qwen3ForSequenceClassification"],
-            "classifier_from_token": ["no", "yes"],
-            "is_original_qwen3_reranker": True,
-        },
         "tensor_parallel_size": 2,
     }
 

tests/models/registry.py

@@ -365,6 +365,10 @@ _SEQUENCE_CLASSIFICATION_EXAMPLE_MODELS = {
 
     # [Cross-encoder]
     "BertForSequenceClassification": _HfExamplesInfo("cross-encoder/ms-marco-MiniLM-L-6-v2", v0_only=True),  # noqa: E501
+    "GteNewForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-multilingual-reranker-base",  # noqa: E501
+                                                       trust_remote_code=True,
+                                                       hf_overrides={
+                                                           "architectures": ["GteNewForSequenceClassification"]}),# noqa: E501
     "ModernBertForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base", v0_only=True), # noqa: E501
     "RobertaForSequenceClassification": _HfExamplesInfo("cross-encoder/quora-roberta-base", v0_only=True),  # noqa: E501
     "XLMRobertaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-m3", v0_only=True),  # noqa: E501

tests/models/utils.py

@@ -3,7 +3,8 @@
 
 import warnings
 from collections.abc import Sequence
-from typing import Any, NamedTuple, Optional, Union
+from dataclasses import dataclass
+from typing import Any, Optional, Union
 
 import torch
 import torch.nn.functional as F
@@ -339,36 +340,43 @@ def softmax(data):
         return F.softmax(data, dim=-1)
 
 
-class EmbedModelInfo(NamedTuple):
+@dataclass
+class ModelInfo:
     name: str
-    is_matryoshka: bool = False
-    matryoshka_dimensions: Optional[list[int]] = None
     architecture: str = ""
     dtype: str = "auto"
+    hf_overrides: Optional[dict[str, Any]] = None
     default_pooling_type: str = ""
     enable_test: bool = True
 
 
+@dataclass
+class EmbedModelInfo(ModelInfo):
+    is_matryoshka: bool = False
+    matryoshka_dimensions: Optional[list[int]] = None
+
+
+@dataclass
 class CLSPoolingEmbedModelInfo(EmbedModelInfo):
     default_pooling_type: str = "CLS"
 
 
+@dataclass
 class LASTPoolingEmbedModelInfo(EmbedModelInfo):
     default_pooling_type: str = "LAST"
 
 
-class RerankModelInfo(NamedTuple):
-    name: str
-    architecture: str = ""
-    dtype: str = "auto"
-    default_pooling_type: str = ""
-    enable_test: bool = True
+@dataclass
+class RerankModelInfo(ModelInfo):
+    pass
 
 
+@dataclass
 class CLSPoolingRerankModelInfo(RerankModelInfo):
     default_pooling_type: str = "CLS"
 
 
+@dataclass
 class LASTPoolingRerankModelInfo(RerankModelInfo):
     default_pooling_type: str = "LAST"
 

vllm/model_executor/models/bert_with_rope.py

@@ -27,12 +27,15 @@ from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.utils import WeightsMapper
+from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
+                                              maybe_prefix)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsQuant
+from ..layers.pooler import ClassifierPooler, DispatchPooler, Pooler
+from .bert import BertPooler
+from .interfaces import SupportsCrossEncoding, SupportsQuant
 from .interfaces_base import default_pooling_type
 
 
@@ -406,9 +409,14 @@ class BertWithRopeEncoder(nn.Module):
 class BertWithRope(nn.Module, SupportsQuant):
     hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
 
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 add_pooling_layer: bool = False):
         super().__init__()
         self.vllm_config = vllm_config
+        self.add_pooling_layer = add_pooling_layer
         self.config = vllm_config.model_config.hf_config
         self.embeddings = BertWithRopeEmbedding(self.config)
         self.encoder = BertWithRopeEncoder(
@@ -416,6 +424,7 @@ class BertWithRope(nn.Module, SupportsQuant):
             bias=getattr(self.config, "bias", True),
             rotary_kwargs=self.config.rotary_kwargs,
             prefix=f"{prefix}.encoder")
+        self.pooler = BertPooler(self.config) if add_pooling_layer else None
 
     def forward(
         self,
@@ -448,7 +457,7 @@ class BertWithRope(nn.Module, SupportsQuant):
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
-            if "pooler" in name:
+            if not self.add_pooling_layer and "pooler" in name:
                 continue
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
@@ -508,8 +517,8 @@ class GteNewModel(BertWithRope):
             "attention.o_proj": "attn.out_proj",
         })
 
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__(vllm_config=vllm_config, prefix=prefix)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "", **kwargs):
+        super().__init__(vllm_config=vllm_config, prefix=prefix, **kwargs)
 
         # GteNewModel only gate_up_proj does not have bias.
         # Hack method learned from vllm/model_executor/models/glm.py
@@ -614,3 +623,65 @@ class JinaRobertaModel(BertWithRope):
                                                    torch.Tensor]]) -> set[str]:
         weights = self.jina_merge_lora_weights(weights)
         return super().load_weights(weights)
+
+
+@default_pooling_type("CLS")
+class GteNewForSequenceClassification(nn.Module, SupportsCrossEncoding):
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.new = GteNewModel(vllm_config=vllm_config,
+                               prefix=prefix,
+                               add_pooling_layer=True)
+        self.classifier = RowParallelLinear(config.hidden_size,
+                                            config.num_labels,
+                                            input_is_parallel=False,
+                                            bias=True,
+                                            quant_config=quant_config,
+                                            prefix=maybe_prefix(
+                                                prefix, "classifier"),
+                                            return_bias=False)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+            "classify":
+            ClassifierPooler(
+                pooling=self.new.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_seq_cls(
+                    vllm_config.model_config),
+            ),
+            "score":
+            ClassifierPooler(
+                pooling=self.new.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_cross_encoder(
+                    vllm_config.model_config),
+            ),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(weights)
+        return loaded_params
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        return self.new(input_ids=input_ids,
+                        positions=positions,
+                        inputs_embeds=inputs_embeds,
+                        intermediate_tensors=intermediate_tensors)

vllm/model_executor/models/config.py

@@ -406,6 +406,7 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
 MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
     "GteModel": SnowflakeGteNewModelConfig,
     "GteNewModel": GteNewModelConfig,
+    "GteNewForSequenceClassification": GteNewModelConfig,
     "NomicBertModel": NomicBertModelConfig,
     "Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
     "Qwen2ForRewardModel": Qwen2ForRewardModelConfig,

vllm/model_executor/models/registry.py

@@ -191,12 +191,14 @@ _EMBEDDING_MODELS = {
 
 _CROSS_ENCODER_MODELS = {
     "BertForSequenceClassification": ("bert", "BertForSequenceClassification"),
+    "GteNewForSequenceClassification": ("bert_with_rope",
+                                        "GteNewForSequenceClassification"),
+    "ModernBertForSequenceClassification": ("modernbert",
+                                            "ModernBertForSequenceClassification"),
     "RobertaForSequenceClassification": ("roberta",
                                          "RobertaForSequenceClassification"),
     "XLMRobertaForSequenceClassification": ("roberta",
                                             "RobertaForSequenceClassification"),
-    "ModernBertForSequenceClassification": ("modernbert",
-                                            "ModernBertForSequenceClassification"),
     # [Auto-converted (see adapters.py)]
     "JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"), # noqa: E501,
 }