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[CI/Build][Doc] Update gte-Qwen2-1.5B-instruct usage (#18683)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
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Cyrus Leung 2025-05-26 11:27:50 +08:00 committed by GitHub
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3 changed files with 2 additions and 18 deletions
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5
docs/models/supported_models.md
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@ -404,10 +404,7 @@ Specified using `--task embed`.
You should manually set mean pooling by passing `--override-pooler-config '{"pooling_type": "MEAN"}'`.
!!! note
The HF implementation of `Alibaba-NLP/gte-Qwen2-1.5B-instruct` is hardcoded to use causal attention despite what is shown in `config.json`. To compare vLLM vs HF results,
you should set `--hf-overrides '{"is_causal": true}'` in vLLM so that the two implementations are consistent with each other.
For both the 1.5B and 7B variants, you also need to enable `--trust-remote-code` for the correct tokenizer to be loaded.
For `Alibaba-NLP/gte-Qwen2-*`, you need to enable `--trust-remote-code` for the correct tokenizer to be loaded.
See [relevant issue on HF Transformers](https://github.com/huggingface/transformers/issues/34882).
!!! note

6
tests/models/language/pooling/test_embedding.py
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@ -15,13 +15,12 @@ from ...utils import check_embeddings_close
marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
pytest.param("intfloat/multilingual-e5-small"),
pytest.param("Alibaba-NLP/gte-Qwen2-7B-instruct"),
pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
# [Decoder-only]
pytest.param("BAAI/bge-multilingual-gemma2",
marks=[pytest.mark.core_model]),
pytest.param("intfloat/e5-mistral-7b-instruct",
marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
# [Cross-Encoder]
pytest.param("sentence-transformers/stsb-roberta-base-v2"),
@ -47,9 +46,6 @@ def test_models(
vllm_extra_kwargs["override_pooler_config"] = \
PoolerConfig(pooling_type="MEAN")
if model == "Alibaba-NLP/gte-Qwen2-1.5B-instruct":
vllm_extra_kwargs["hf_overrides"] = {"is_causal": True}
# The example_prompts has ending "\n", for example:
# "Write a short story about a robot that dreams for the first time.\n"
# sentence_transformers will strip the input texts, see:

9
tests/models/language/pooling/test_gte.py
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@ -45,9 +45,6 @@ MODELS = [
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",
@ -61,9 +58,6 @@ def test_models_mteb(hf_runner, vllm_runner,
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"]}
@ -81,9 +75,6 @@ def test_models_correctness(hf_runner, vllm_runner, model_info: EmbedModelInfo,
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"]}