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treat do_lower_case in the same way as the sentence-transformers library (#11815)
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
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@ -35,6 +35,7 @@ class MockModelConfig:
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logits_processor_pattern = None
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diff_sampling_param: Optional[dict] = None
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allowed_local_media_path: str = ""
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encoder_config = None
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def get_diff_sampling_param(self):
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return self.diff_sampling_param or {}
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@ -15,6 +15,7 @@ from ..utils import check_embeddings_close
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# [Encoder-only]
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pytest.param("BAAI/bge-base-en-v1.5",
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marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
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pytest.param("sentence-transformers/all-MiniLM-L12-v2"),
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pytest.param("intfloat/multilingual-e5-large"),
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# [Encoder-decoder]
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pytest.param("intfloat/e5-mistral-7b-instruct",
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@ -160,6 +160,11 @@ class OpenAIServing:
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]],
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add_special_tokens: bool,
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) -> TextTokensPrompt:
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if (self.model_config.encoder_config is not None
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and self.model_config.encoder_config.get(
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"do_lower_case", False)):
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prompt = prompt.lower()
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if truncate_prompt_tokens is None:
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encoded = tokenizer(prompt, add_special_tokens=add_special_tokens)
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else:
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@ -190,6 +190,12 @@ class InputPreprocessor:
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# on the task and language of their request. Also needed to avoid
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# appending an EOS token to the prompt which disrupts generation.
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add_special_tokens = False
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if (self.model_config.encoder_config is not None
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and self.model_config.encoder_config.get(
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"do_lower_case", False)):
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prompt = prompt.lower()
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return tokenizer.encode(request_id=request_id,
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prompt=prompt,
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lora_request=lora_request,
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@ -26,11 +26,6 @@ def init_tokenizer_from_configs(model_config: ModelConfig,
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trust_remote_code=model_config.trust_remote_code,
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revision=model_config.tokenizer_revision)
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if (model_config.encoder_config is not None
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and "do_lower_case" in model_config.encoder_config):
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init_kwargs["do_lower_case"] = model_config.encoder_config[
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"do_lower_case"]
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return get_tokenizer_group(parallel_config.tokenizer_pool_config,
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**init_kwargs)
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