[New Model]: support GTE NewModel (#17986)

docs/source/models/supported_models.md

@@ -701,12 +701,22 @@ Specified using `--task embed`.
   * ✅︎
   * ✅︎
 - * `GteModel`
-  * GteModel
+  * Arctic-Embed-2.0-M
   * `Snowflake/snowflake-arctic-embed-m-v2.0`.
   *
   * ︎
+- * `GteNewModel`
+  * mGTE-TRM (see note)
+  * `Alibaba-NLP/gte-multilingual-base`, etc.
+  * ︎
+  * ︎
+- * `ModernBertModel`
+  * ModernBERT-based
+  * `Alibaba-NLP/gte-modernbert-base`, etc.
+  * ︎
+  * ︎
 - * `NomicBertModel`
-  * NomicBertModel
+  * Nomic BERT
   * `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc.
   * ︎
   * ︎
@@ -749,6 +759,10 @@ See [relevant issue on HF Transformers](https://github.com/huggingface/transform
 `jinaai/jina-embeddings-v3` supports multiple tasks through lora, while vllm temporarily only supports text-matching tasks by merging lora weights.
 :::
 
+:::{note}
+The second-generation GTE model (mGTE-TRM) is named `NewModel`. The name `NewModel` is too generic, you should set `--hf-overrides '{"architectures": ["GteNewModel"]}'` to specify the use of the `GteNewModel` architecture.
+:::
+
 If your model is not in the above list, we will try to automatically convert the model using
 {func}`~vllm.model_executor.models.adapters.as_embedding_model`. By default, the embeddings
 of the whole prompt are extracted from the normalized hidden state corresponding to the last token.

tests/models/language/pooling/mteb_utils.py

@@ -7,6 +7,7 @@ import numpy as np
 import pytest
 
 from tests.models.utils import EmbedModelInfo
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
 
 # Most models on the STS12 task (See #17175):
 # - Model implementation and minor changes in tensor dtype
@@ -77,16 +78,22 @@ def run_mteb_embed_task_st(model_name, tasks):
     return run_mteb_embed_task(model, tasks)
 
 
-def mteb_test_embed_models(hf_runner, vllm_runner, model_info: EmbedModelInfo):
+def mteb_test_embed_models(hf_runner,
+                           vllm_runner,
+                           model_info: EmbedModelInfo,
+                           vllm_extra_kwargs=None):
     if not model_info.enable_test:
         # A model family has many models with the same architecture,
         # and we don't need to test each one.
         pytest.skip("Skipping test.")
 
+    vllm_extra_kwargs = vllm_extra_kwargs or {}
+
     with vllm_runner(model_info.name,
                      task="embed",
                      max_model_len=None,
-                     dtype=model_info.dtype) as vllm_model:
+                     dtype=model_info.dtype,
+                     **vllm_extra_kwargs) as vllm_model:
 
         if model_info.architecture:
             assert (model_info.architecture
@@ -99,9 +106,9 @@ def mteb_test_embed_models(hf_runner, vllm_runner, model_info: EmbedModelInfo):
             vllm_model.model.llm_engine.model_config.hf_config, "torch_dtype",
             vllm_dtype)
 
-    with hf_runner(model_info.name,
-                   is_sentence_transformer=True,
-                   dtype=model_dtype) as hf_model:
+    with set_default_torch_dtype(model_dtype) and hf_runner(
+            model_info.name, is_sentence_transformer=True,
+            dtype=model_dtype) as hf_model:
         st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
 
     print("VLLM:", vllm_dtype, vllm_main_score)

tests/models/language/pooling/test_gte.py

@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any
+
+import pytest
+
+from ...utils import EmbedModelInfo, run_embedding_correctness_test
+
+MODELS = [
+    ########## BertModel
+    EmbedModelInfo("thenlper/gte-large",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=True),
+    EmbedModelInfo("thenlper/gte-base",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=False),
+    EmbedModelInfo("thenlper/gte-small",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=False),
+    EmbedModelInfo("thenlper/gte-large-zh",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=False),
+    EmbedModelInfo("thenlper/gte-base-zh",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=False),
+    EmbedModelInfo("thenlper/gte-small-zh",
+                   architecture="BertModel",
+                   dtype="float32",
+                   enable_test=False),
+    ########### NewModel
+    EmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
+                   architecture="GteNewModel",
+                   enable_test=True),
+    EmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
+                   architecture="GteNewModel",
+                   enable_test=True),
+    EmbedModelInfo("Alibaba-NLP/gte-large-en-v1.5",
+                   architecture="GteNewModel",
+                   enable_test=True),
+    ########### Qwen2ForCausalLM
+    EmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+                   architecture="Qwen2ForCausalLM",
+                   enable_test=True),
+    EmbedModelInfo("Alibaba-NLP/gte-Qwen2-7B-instruct",
+                   architecture="Qwen2ForCausalLM",
+                   enable_test=False),
+    ########## ModernBertModel
+    EmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
+                   architecture="ModernBertModel",
+                   enable_test=True),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_models_mteb(hf_runner, vllm_runner,
+                     model_info: EmbedModelInfo) -> None:
+    pytest.skip("Skipping mteb test.")
+
+    from .mteb_utils import mteb_test_embed_models
+
+    vllm_extra_kwargs: dict[str, Any] = {}
+    if model_info.name == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
+        vllm_extra_kwargs["hf_overrides"] = {"is_causal": True}
+
+    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)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_models_correctness(hf_runner, vllm_runner, model_info: EmbedModelInfo,
+                            example_prompts) -> None:
+    if not model_info.enable_test:
+        pytest.skip("Skipping test.")
+
+    # ST will strip the input texts, see test_embedding.py
+    example_prompts = [str(s).strip() for s in example_prompts]
+
+    vllm_extra_kwargs: dict[str, Any] = {}
+    if model_info.name == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
+        vllm_extra_kwargs["hf_overrides"] = {"is_causal": True}
+
+    if model_info.architecture == "GteNewModel":
+        vllm_extra_kwargs["hf_overrides"] = {"architectures": ["GteNewModel"]}
+
+    with vllm_runner(model_info.name,
+                     task="embed",
+                     dtype=model_info.dtype,
+                     max_model_len=None,
+                     **vllm_extra_kwargs) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+
+    with hf_runner(
+            model_info.name,
+            dtype=model_info.dtype,
+            is_sentence_transformer=True,
+    ) as hf_model:
+        run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)

tests/models/language/pooling/test_nomic.py

@@ -23,6 +23,7 @@ MODELS = [
 @pytest.mark.parametrize("model_info", MODELS)
 def test_models_mteb(hf_runner, vllm_runner,
                      model_info: EmbedModelInfo) -> None:
+    pytest.skip("Skipping mteb test.")
     from .mteb_utils import mteb_test_embed_models
     mteb_test_embed_models(hf_runner, vllm_runner, model_info)
 
@@ -33,6 +34,9 @@ def test_models_correctness(hf_runner, vllm_runner, model_info: EmbedModelInfo,
     if not model_info.enable_test:
         pytest.skip("Skipping test.")
 
+    # ST will strip the input texts, see test_embedding.py
+    example_prompts = [str(s).strip() for s in example_prompts]
+
     with vllm_runner(model_info.name,
                      task="embed",
                      dtype=model_info.dtype,

tests/models/language/pooling/test_snowflake_arctic_embed.py

@@ -46,6 +46,7 @@ def test_models_mteb(
     vllm_runner,
     model_info: EmbedModelInfo,
 ) -> None:
+    pytest.skip("Skipping mteb test.")
     from .mteb_utils import mteb_test_embed_models
     mteb_test_embed_models(hf_runner, vllm_runner, model_info)
 
@@ -60,6 +61,9 @@ def test_models_correctness(
     if not model_info.enable_test:
         pytest.skip("Skipping test.")
 
+    # ST will strip the input texts, see test_embedding.py
+    example_prompts = [str(s).strip() for s in example_prompts]
+
     with vllm_runner(model_info.name,
                      task="embed",
                      dtype=model_info.dtype,

tests/models/registry.py

@@ -256,11 +256,17 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "GritLM": _HfExamplesInfo("parasail-ai/GritLM-7B-vllm"),
     "GteModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
                                                trust_remote_code=True),
+    "GteNewModel": _HfExamplesInfo("Alibaba-NLP/gte-base-en-v1.5",
+                                   trust_remote_code=True,
+                                   hf_overrides={"architectures":
+                                                     ["GteNewModel"]}),
     "InternLM2ForRewardModel": _HfExamplesInfo("internlm/internlm2-1_8b-reward",
                                                trust_remote_code=True),
     "JambaForSequenceClassification": _HfExamplesInfo("ai21labs/Jamba-tiny-reward-dev"),  # noqa: E501
     "LlamaModel": _HfExamplesInfo("llama", is_available_online=False),
     "MistralModel": _HfExamplesInfo("intfloat/e5-mistral-7b-instruct"),
+    "ModernBertModel": _HfExamplesInfo("Alibaba-NLP/gte-modernbert-base",
+                                trust_remote_code=True),
     "NomicBertModel": _HfExamplesInfo("Snowflake/snowflake-arctic-embed-m-long",  # noqa: E501
                                                trust_remote_code=True),
     "Qwen2Model": _HfExamplesInfo("ssmits/Qwen2-7B-Instruct-embed-base"),

vllm/model_executor/layers/activation.py

@@ -354,7 +354,7 @@ def get_act_fn(act_fn_name: str) -> nn.Module:
 _ACTIVATION_AND_MUL_REGISTRY = LazyDict({
     "gelu": lambda: GeluAndMul(),
     "silu": lambda: SiluAndMul(),
-    "gelu_and_mul": lambda: GeluAndMul(),
+    "geglu": lambda: GeluAndMul(),
 })
 
 

vllm/model_executor/layers/rotary_embedding.py

@@ -456,6 +456,40 @@ class LinearScalingRotaryEmbedding(RotaryEmbedding):
         return self._scaling_factor_to_offset
 
 
+class NTKScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with fixed and mixed NTK scaling.
+    https://kexue.fm/archives/9706 """
+
+    def __init__(self,
+                 head_size: int,
+                 rotary_dim: int,
+                 max_position_embeddings: int,
+                 base: int,
+                 is_neox_style: bool,
+                 scaling_factor: float,
+                 dtype: torch.dtype,
+                 mixed_b: Optional[float] = None) -> None:
+        self.scaling_factor = scaling_factor
+        self.mixed_b = mixed_b
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        base = self.base * (self.scaling_factor if self.mixed_b is None else 1)
+        inv_freq = super()._compute_inv_freq(base)
+
+        if self.mixed_b is None:
+            inv_freq = inv_freq / self.scaling_factor**(2 / self.rotary_dim)
+        else:
+            a = torch.tensor(self.scaling_factor).log() / (self.rotary_dim /
+                                                           2)**self.mixed_b
+            lambda_1_m = (a * torch.arange(
+                1, self.rotary_dim // 2 + 1).float()**self.mixed_b).exp()
+            inv_freq = inv_freq / lambda_1_m
+
+        return inv_freq
+
+
 class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
     """RotaryEmbedding extended with Dynamic NTK scaling.
 
@@ -1765,6 +1799,14 @@ def get_rope(
                                                       max_position, base,
                                                       is_neox_style,
                                                       scaling_factor, dtype)
+        elif scaling_type == "ntk":
+            scaling_factor = rope_scaling["factor"]
+            mixed_b = rope_scaling.get('mixed_b', None)
+            rotary_emb = NTKScalingRotaryEmbedding(head_size, rotary_dim,
+                                                   max_position, base,
+                                                   is_neox_style,
+                                                   scaling_factor, dtype,
+                                                   mixed_b)
         elif scaling_type == "dynamic":
             scaling_factor = rope_scaling["factor"]
             rotary_emb = DynamicNTKScalingRotaryEmbedding(

vllm/model_executor/models/bert_with_rope.py

@@ -32,11 +32,18 @@ class BertWithRopeEmbedding(nn.Module):
     def __init__(self, config: PretrainedConfig):
 
         super().__init__()
-        assert config.type_vocab_size > 0
+        if config.position_embedding_type not in ["rope", "rotary"]:
+            raise ValueError("Only 'rotary'('rope') position_embedding_type" +
+                             " is supported")
+
         self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
                                                       config.hidden_size)
-        self.token_type_embeddings = VocabParallelEmbedding(
-            config.type_vocab_size, config.hidden_size)
+        if config.type_vocab_size > 0:
+            self.token_type_embeddings = VocabParallelEmbedding(
+                config.type_vocab_size, config.hidden_size)
+        else:
+            self.token_type_embeddings = None
+
         self.LayerNorm = nn.LayerNorm(config.hidden_size,
                                       eps=config.layer_norm_eps)
 
@@ -47,13 +54,17 @@ class BertWithRopeEmbedding(nn.Module):
     ) -> torch.Tensor:
         input_shape = input_ids.size()
         inputs_embeds = self.word_embeddings(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros(input_shape,
-                                         dtype=torch.long,
-                                         device=inputs_embeds.device)
 
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-        embeddings = inputs_embeds + token_type_embeddings
+        embeddings = inputs_embeds
+        if self.token_type_embeddings is not None:
+            if token_type_ids is None:
+                token_type_ids = torch.zeros(input_shape,
+                                             dtype=torch.long,
+                                             device=inputs_embeds.device)
+
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings += token_type_embeddings
+
         embeddings = self.LayerNorm(embeddings)
         return embeddings
 
@@ -321,7 +332,7 @@ class BertWithRopeBlock(nn.Module):
         if moe:
             self.mlp = NomicMoELayer(config=config, )
         else:
-            if config.hidden_act in ["silu", "gelu_and_mul"]:
+            if config.hidden_act in ["silu", "geglu"]:
                 self.mlp = BertWithRopeGatedMLP(
                     hidden_size=config.hidden_size,
                     intermediate_size=config.intermediate_size,
@@ -390,6 +401,7 @@ class BertWithRope(nn.Module, SupportsV0Only, SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
+        self.vllm_config = vllm_config
         self.config = self.config_verify(vllm_config)
         self.embeddings = BertWithRopeEmbedding(self.config)
         self.encoder = BertWithRopeEncoder(
@@ -420,7 +432,7 @@ class BertWithRope(nn.Module, SupportsV0Only, SupportsQuant):
                                                    torch.Tensor]]) -> Set[str]:
         weights = self.hf_to_vllm_mapper.apply(weights)
 
-        if self.config.hidden_act in ["silu", "gelu_and_mul"]:
+        if self.config.hidden_act in ["silu", "geglu"]:
             stacked_params_mapping = [
                 # (param_name, shard_name, shard_id)
                 ("gate_up_proj", "gate_proj", 0),
@@ -458,6 +470,8 @@ class BertWithRope(nn.Module, SupportsV0Only, SupportsQuant):
 
 
 class NomicBertModel(BertWithRope):
+    # for https://huggingface.co/nomic-ai/nomic-bert-2048
+
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             "emb_ln": "embeddings.LayerNorm",
@@ -475,6 +489,9 @@ class NomicBertModel(BertWithRope):
 
         assert config.__class__.__name__ == "NomicBertConfig"
         assert config.activation_function in ["swiglu", "gelu"]
+        config.position_embedding_type = getattr(config,
+                                                 "position_embedding_type",
+                                                 "rope")
 
         if config.activation_function == "swiglu":
             config.hidden_act = "silu"
@@ -512,10 +529,13 @@ class NomicBertModel(BertWithRope):
         return config
 
 
-class GteModel(BertWithRope):
+class GteNewModel(BertWithRope):
+    # for https://huggingface.co/Alibaba-NLP/new-impl
+
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
-            "layer": 'layers',
+            "new.": "",
+            "layer": "layers",
             "attention.qkv_proj": "attn.qkv_proj",
             "attention.o_proj": "attn.out_proj",
         })
@@ -523,7 +543,7 @@ class GteModel(BertWithRope):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)
 
-        # GteModel only gate_up_proj does not have bias.
+        # GteNewModel only gate_up_proj does not have bias.
         # Hack method learned from vllm/model_executor/models/glm.py
         for layer in self.encoder.layers:
             layer.mlp.gate_up_proj.bias = None
@@ -532,12 +552,10 @@ class GteModel(BertWithRope):
     def config_verify(self, vllm_config):
         config = vllm_config.model_config.hf_config
 
-        assert config.__class__.__name__ == "GteConfig"
-        assert config.position_embedding_type == "rope"
+        assert config.__class__.__name__ == "NewConfig"
         assert config.hidden_act == "gelu"
 
-        config.position_embedding_type = "rotary"
-        config.hidden_act = "gelu_and_mul"
+        config.hidden_act = "geglu"
 
         head_dim = config.hidden_size // config.num_attention_heads
         config.rotary_kwargs = {
@@ -559,13 +577,52 @@ class GteModel(BertWithRope):
             else:
                 yield name, weight
 
+    def ignore_unnecessary_layers(self,
+                                  weights: Iterable[Tuple[str, torch.Tensor]]):
+        for name, weight in weights:
+            if name.startswith("classifier"):
+                continue
+            yield name, weight
+
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
+        weights = self.ignore_unnecessary_layers(weights)
         weights = self.split_up_gate_proj(weights)
         return super().load_weights(weights)
 
 
+class SnowflakeGteNewModel(GteNewModel):
+    # for Snowflake/snowflake-arctic-embed-m-v2.0
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            "layer": "layers",
+            "attention.qkv_proj": "attn.qkv_proj",
+            "attention.o_proj": "attn.out_proj",
+        })
+
+    def config_verify(self, vllm_config):
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "GteConfig"
+        assert config.hidden_act == "gelu"
+
+        config.hidden_act = "geglu"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+        return config
+
+
 class JinaRobertaModel(BertWithRope):
+    # for https://huggingface.co/jinaai/jina-embeddings-v3
+
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             "emb_ln": "embeddings.LayerNorm",
@@ -579,6 +636,9 @@ class JinaRobertaModel(BertWithRope):
 
     def config_verify(self, vllm_config):
         config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "XLMRobertaFlashConfig"
+
         head_dim = config.hidden_size // config.num_attention_heads
         config.rotary_kwargs = {
             "head_size": head_dim,
@@ -611,6 +671,7 @@ class JinaRobertaModel(BertWithRope):
         # This is a temporary solution until we have a better way to handle
 
         scaling = self.config.lora_alpha / self.config.lora_rank
+        device = self.vllm_config.device_config.device
 
         weights = {name: weight for name, weight in weights}
 
@@ -628,13 +689,13 @@ class JinaRobertaModel(BertWithRope):
                 weight_name = name[:-len(o)]
 
                 if "embeddings" in weight_name:
-                    B = weights[weight_name + a][i].cuda().float()
-                    A = weights[weight_name + b][i].cuda().float()
+                    B = weights[weight_name + a][i].to(device).float()
+                    A = weights[weight_name + b][i].to(device).float()
                 else:
-                    B = weights[weight_name + b][i].cuda().float()
-                    A = weights[weight_name + a][i].cuda().float()
+                    B = weights[weight_name + b][i].to(device).float()
+                    A = weights[weight_name + a][i].to(device).float()
 
-                weight = (weights[weight_name + o].cuda() +
+                weight = (weights[weight_name + o].to(device) +
                           torch.matmul(B, A).view(shape) * scaling)
                 weight = weight.cpu().to(dtype)
 

vllm/model_executor/models/modernbert.py

@@ -230,9 +230,12 @@ class ModernBertModel(nn.Module):
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
+        positions: Optional[torch.Tensor] = None,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
     ) -> torch.Tensor:
+        position_ids = positions if positions is not None else position_ids
         if inputs_embeds is not None:
             hidden_states = inputs_embeds
         else:

vllm/model_executor/models/registry.py

@@ -127,7 +127,8 @@ _EMBEDDING_MODELS = {
     "Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
     "GritLM": ("gritlm", "GritLM"),
-    "GteModel": ("bert_with_rope", "GteModel"),
+    "GteModel": ("bert_with_rope", "SnowflakeGteNewModel"),
+    "GteNewModel": ("bert_with_rope", "GteNewModel"),
     "InternLM2ForRewardModel": ("internlm2", "InternLM2ForRewardModel"),
     "JambaForSequenceClassification": ("jamba", "JambaForSequenceClassification"),  # noqa: E501
     "LlamaModel": ("llama", "LlamaForCausalLM"),
@@ -137,6 +138,7 @@ _EMBEDDING_MODELS = {
         if arch == "LlamaForCausalLM"
     },
     "MistralModel": ("llama", "LlamaForCausalLM"),
+    "ModernBertModel": ("modernbert", "ModernBertModel"),
     "NomicBertModel": ("bert_with_rope", "NomicBertModel"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
     "Qwen2Model": ("qwen2", "Qwen2EmbeddingModel"),