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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import pytest
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from ...utils import EmbedModelInfo, run_embedding_correctness_test
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MODELS = [
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EmbedModelInfo("nomic-ai/nomic-embed-text-v1",
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architecture="NomicBertModel",
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dtype="float32",
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enable_test=True),
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EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5",
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architecture="NomicBertModel",
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dtype="float32",
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enable_test=False),
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EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe",
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architecture="NomicBertModel",
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dtype="float32",
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enable_test=True)
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]
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@pytest.mark.parametrize("model_info", MODELS)
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def test_models_mteb(hf_runner, vllm_runner,
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model_info: EmbedModelInfo) -> None:
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from .mteb_utils import mteb_test_embed_models
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mteb_test_embed_models(hf_runner, vllm_runner, model_info)
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@pytest.mark.parametrize("model_info", MODELS)
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def test_models_correctness(hf_runner, vllm_runner, model_info: EmbedModelInfo,
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example_prompts) -> None:
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if not model_info.enable_test:
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pytest.skip("Skipping test.")
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with vllm_runner(model_info.name,
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task="embed",
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dtype=model_info.dtype,
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max_model_len=None) as vllm_model:
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vllm_outputs = vllm_model.encode(example_prompts)
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with hf_runner(
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model_info.name,
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dtype=model_info.dtype,
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is_sentence_transformer=True,
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) as hf_model:
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run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
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