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[Model] Pooling models default to using chunked prefill & prefix caching if supported. (#20930)

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Signed-off-by: wang.yuqi <noooop@126.com>
This commit is contained in:
wang.yuqi
2025-08-12 00:41:37 +08:00
committed by GitHub
parent 16fb668b61
commit 84cf78acee
31 changed files with 452 additions and 261 deletions
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12
tests/models/language/pooling/mteb_utils.py
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@@ -177,9 +177,12 @@ def mteb_test_embed_models(hf_runner,
max_model_len=None,
**vllm_extra_kwargs) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
if model_info.architecture:
assert (model_info.architecture
in vllm_model.llm.llm_engine.model_config.architectures)
assert model_info.architecture in model_config.architectures
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)
@@ -286,7 +289,12 @@ def mteb_test_rerank_models(hf_runner,
**vllm_extra_kwargs) as vllm_model:
model_config = vllm_model.llm.llm_engine.model_config
if model_info.architecture:
assert (model_info.architecture in model_config.architectures)
assert model_config.hf_config.num_labels == 1
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,

93
tests/models/language/pooling/test_auto_prefix_cache_support.py Normal file
View File

@@ -0,0 +1,93 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import torch
from transformers import AutoModelForSequenceClassification
from tests.models.language.pooling.embed_utils import (
run_embedding_correctness_test)
@pytest.mark.parametrize(
"model",
["jason9693/Qwen2.5-1.5B-apeach"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_classify_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
) -> None:
example_prompts = example_prompts * 2
with vllm_runner(model,
max_model_len=512,
dtype=dtype,
enable_prefix_caching=True) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
vllm_outputs = vllm_model.classify(example_prompts)
with hf_runner(model,
dtype=dtype,
auto_cls=AutoModelForSequenceClassification) as hf_model:
hf_outputs = hf_model.classify(example_prompts)
for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
hf_output = torch.tensor(hf_output)
vllm_output = torch.tensor(vllm_output)
assert torch.allclose(hf_output, vllm_output,
1e-3 if dtype == "float" else 1e-2)
@pytest.mark.parametrize(
"model",
["Qwen/Qwen3-Embedding-0.6B"],
)
@pytest.mark.parametrize("dtype", ["half"])
def test_embed_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
):
example_prompts = [str(s).strip() for s in example_prompts] * 2
with vllm_runner(
model,
runner="pooling",
max_model_len=None,
enable_prefix_caching=True,
) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert cache_config.enable_prefix_caching
vllm_outputs = vllm_model.embed(example_prompts)
with hf_runner(
model,
is_sentence_transformer=True,
) as hf_model:
run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
@pytest.mark.parametrize(
"model",
[
"intfloat/e5-small",
"Alibaba-NLP/gte-Qwen2-1.5B-instruct", # is_causal == False
"papluca/xlm-roberta-base-language-detection",
])
@pytest.mark.parametrize("dtype", ["half"])
def test_non_causal_models(hf_runner, vllm_runner, example_prompts, model: str,
dtype: str) -> None:
with vllm_runner(model,
max_model_len=512,
dtype=dtype,
enable_prefix_caching=True) as vllm_model:
cache_config = vllm_model.llm.llm_engine.cache_config
assert not cache_config.enable_prefix_caching

117
tests/models/language/pooling/test_baai.py
View File

@@ -2,73 +2,78 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from ...utils import EmbedModelInfo, RerankModelInfo
from ...utils import (CLSPoolingEmbedModelInfo, CLSPoolingRerankModelInfo,
EmbedModelInfo, LASTPoolingEmbedModelInfo,
RerankModelInfo)
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
MODELS = [
########## BertModel
EmbedModelInfo("BAAI/bge-base-en",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("BAAI/bge-base-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-en",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-en",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh-noinstruct",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-base-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-base-zh-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-small-zh-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-en-v1.5",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("BAAI/bge-large-zh-v1.5",
architecture="BertModel",
enable_test=False),
CLSPoolingEmbedModelInfo("BAAI/bge-base-en",
architecture="BertModel",
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
EmbedModelInfo("BAAI/bge-m3",
architecture="XLMRobertaModel",
enable_test=True),
CLSPoolingEmbedModelInfo("BAAI/bge-m3",
architecture="XLMRobertaModel",
enable_test=True),
########## Qwen2Model
EmbedModelInfo("BAAI/bge-code-v1",
architecture="Qwen2Model",
dtype="float32",
enable_test=True),
LASTPoolingEmbedModelInfo("BAAI/bge-code-v1",
architecture="Qwen2Model",
dtype="float32",
enable_test=True),
]
RERANK_MODELS = [
########## XLMRobertaForSequenceClassification
RerankModelInfo("BAAI/bge-reranker-base",
architecture="XLMRobertaForSequenceClassification",
enable_test=True),
RerankModelInfo("BAAI/bge-reranker-large",
architecture="XLMRobertaForSequenceClassification",
enable_test=False),
RerankModelInfo("BAAI/bge-reranker-v2-m3",
architecture="XLMRobertaForSequenceClassification",
enable_test=False)
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-base",
architecture="XLMRobertaForSequenceClassification",
enable_test=True),
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-large",
architecture="XLMRobertaForSequenceClassification",
enable_test=False),
CLSPoolingRerankModelInfo(
"BAAI/bge-reranker-v2-m3",
architecture="XLMRobertaForSequenceClassification",
enable_test=False)
]

8
tests/models/language/pooling/test_bge_reranker_v2_gemma.py
View File

@@ -8,12 +8,12 @@ import torch
from tests.conftest import HfRunner
from .mteb_utils import (RerankModelInfo, VllmMtebEncoder,
mteb_test_rerank_models)
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import VllmMtebEncoder, mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("BAAI/bge-reranker-v2-gemma",
architecture="GemmaForSequenceClassification"),
LASTPoolingRerankModelInfo("BAAI/bge-reranker-v2-gemma",
architecture="GemmaForSequenceClassification"),
]
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

12
tests/models/language/pooling/test_cross_encoder.py
View File

@@ -2,13 +2,15 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import (CLSPoolingRerankModelInfo, LASTPoolingRerankModelInfo,
RerankModelInfo)
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("cross-encoder/ms-marco-TinyBERT-L-2-v2",
architecture="BertForSequenceClassification"),
RerankModelInfo("tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
architecture="Qwen3ForSequenceClassification")
CLSPoolingRerankModelInfo("cross-encoder/ms-marco-TinyBERT-L-2-v2",
architecture="BertForSequenceClassification"),
LASTPoolingRerankModelInfo("tomaarsen/Qwen3-Reranker-0.6B-seq-cls",
architecture="Qwen3ForSequenceClassification")
]

87
tests/models/language/pooling/test_gte.py
View File

@@ -4,57 +4,58 @@ from typing import Any
import pytest
from ...utils import check_transformers_version
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import (CLSPoolingEmbedModelInfo, EmbedModelInfo,
LASTPoolingEmbedModelInfo, check_transformers_version)
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
########## BertModel
EmbedModelInfo("thenlper/gte-large",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("thenlper/gte-base",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-small",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-large-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-base-zh",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("thenlper/gte-small-zh",
architecture="BertModel",
enable_test=False),
CLSPoolingEmbedModelInfo("thenlper/gte-large",
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
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),
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
architecture="GteNewModel",
enable_test=True),
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-base-en-v1.5",
architecture="GteNewModel",
enable_test=True),
CLSPoolingEmbedModelInfo("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),
LASTPoolingEmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
architecture="Qwen2ForCausalLM",
enable_test=True),
########## ModernBertModel
EmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
architecture="ModernBertModel",
enable_test=True),
CLSPoolingEmbedModelInfo("Alibaba-NLP/gte-modernbert-base",
architecture="ModernBertModel",
enable_test=True),
########## Qwen3ForCausalLM
EmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=True),
EmbedModelInfo("Qwen/Qwen3-Embedding-4B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=False),
LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-0.6B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=True),
LASTPoolingEmbedModelInfo("Qwen/Qwen3-Embedding-4B",
architecture="Qwen3ForCausalLM",
dtype="float32",
enable_test=False),
]

44
tests/models/language/pooling/test_intfloat.py
View File

@@ -2,34 +2,34 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from ...utils import EmbedModelInfo
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
########## BertModel
EmbedModelInfo("intfloat/e5-small",
architecture="BertModel",
enable_test=True),
EmbedModelInfo("intfloat/e5-base",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("intfloat/e5-large",
architecture="BertModel",
enable_test=False),
EmbedModelInfo("intfloat/multilingual-e5-small",
architecture="BertModel",
enable_test=False),
CLSPoolingEmbedModelInfo("intfloat/e5-small",
architecture="BertModel",
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
EmbedModelInfo("intfloat/multilingual-e5-base",
architecture="XLMRobertaModel",
enable_test=True),
EmbedModelInfo("intfloat/multilingual-e5-large",
architecture="XLMRobertaModel",
enable_test=False),
EmbedModelInfo("intfloat/multilingual-e5-large-instruct",
architecture="XLMRobertaModel",
enable_test=False),
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-base",
architecture="XLMRobertaModel",
enable_test=True),
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-large",
architecture="XLMRobertaModel",
enable_test=False),
CLSPoolingEmbedModelInfo("intfloat/multilingual-e5-large-instruct",
architecture="XLMRobertaModel",
enable_test=False),
]

14
tests/models/language/pooling/test_jina.py
View File

@@ -6,20 +6,22 @@ import pytest
from vllm import PoolingParams
from ...utils import EmbedModelInfo, RerankModelInfo
from ...utils import (CLSPoolingEmbedModelInfo, CLSPoolingRerankModelInfo,
EmbedModelInfo, RerankModelInfo)
from .embed_utils import (check_embeddings_close,
correctness_test_embed_models, matryoshka_fy)
from .mteb_utils import mteb_test_embed_models, mteb_test_rerank_models
EMBEDDING_MODELS = [
EmbedModelInfo("jinaai/jina-embeddings-v3",
architecture="XLMRobertaModel",
is_matryoshka=True)
CLSPoolingEmbedModelInfo("jinaai/jina-embeddings-v3",
architecture="XLMRobertaModel",
is_matryoshka=True)
]
RERANK_MODELS = [
RerankModelInfo("jinaai/jina-reranker-v2-base-multilingual",
architecture="XLMRobertaForSequenceClassification")
CLSPoolingRerankModelInfo(
"jinaai/jina-reranker-v2-base-multilingual",
architecture="XLMRobertaForSequenceClassification")
]

15
tests/models/language/pooling/test_mxbai_rerank.py
View File

@@ -7,15 +7,16 @@ import torch
from tests.conftest import HfRunner
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("mixedbread-ai/mxbai-rerank-base-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=True),
RerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=False)
LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-base-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=True),
LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
architecture="Qwen2ForSequenceClassification",
enable_test=False)
]

27
tests/models/language/pooling/test_nomic.py
View File

@@ -3,22 +3,23 @@
import pytest
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo("nomic-ai/nomic-embed-text-v1",
architecture="NomicBertModel",
enable_test=True),
EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5",
architecture="NomicBertModel",
enable_test=False),
EmbedModelInfo("nomic-ai/CodeRankEmbed",
architecture="NomicBertModel",
enable_test=False),
EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe",
architecture="NomicBertModel",
enable_test=True)
CLSPoolingEmbedModelInfo("nomic-ai/nomic-embed-text-v1",
architecture="NomicBertModel",
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",
enable_test=True)
]

15
tests/models/language/pooling/test_qwen3_reranker.py
View File

@@ -8,15 +8,16 @@ import torch
from tests.conftest import HfRunner
from tests.utils import multi_gpu_test
from .mteb_utils import RerankModelInfo, mteb_test_rerank_models
from ...utils import LASTPoolingRerankModelInfo, RerankModelInfo
from .mteb_utils import mteb_test_rerank_models
RERANK_MODELS = [
RerankModelInfo("Qwen/Qwen3-Reranker-0.6B",
architecture="Qwen3ForSequenceClassification",
enable_test=True),
RerankModelInfo("Qwen/Qwen3-Reranker-4B",
architecture="Qwen3ForSequenceClassification",
enable_test=False)
LASTPoolingRerankModelInfo("Qwen/Qwen3-Reranker-0.6B",
architecture="Qwen3ForSequenceClassification",
enable_test=True),
LASTPoolingRerankModelInfo("Qwen/Qwen3-Reranker-4B",
architecture="Qwen3ForSequenceClassification",
enable_test=False)
]

67
tests/models/language/pooling/test_snowflake_arctic_embed.py
View File

@@ -3,42 +3,43 @@
import pytest
from .embed_utils import EmbedModelInfo, correctness_test_embed_models
from ...utils import CLSPoolingEmbedModelInfo, EmbedModelInfo
from .embed_utils import correctness_test_embed_models
from .mteb_utils import mteb_test_embed_models
MODELS = [
EmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
is_matryoshka=False,
architecture="BertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-s",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-long",
is_matryoshka=False,
architecture="NomicBertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l",
is_matryoshka=False,
architecture="BertModel",
enable_test=False),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v1.5",
is_matryoshka=True,
architecture="BertModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
is_matryoshka=True,
architecture="XLMRobertaModel",
enable_test=True),
EmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
is_matryoshka=True,
architecture="GteModel",
enable_test=True),
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
is_matryoshka=False,
architecture="BertModel",
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",
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",
enable_test=True),
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-l-v2.0",
is_matryoshka=True,
architecture="XLMRobertaModel",
enable_test=True),
CLSPoolingEmbedModelInfo("Snowflake/snowflake-arctic-embed-m-v2.0",
is_matryoshka=True,
architecture="GteModel",
enable_test=True),
]