53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
import os
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from typing import Union
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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AutoTokenizer,
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PreTrainedModel,
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PreTrainedTokenizer,
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)
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import custom_models
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custom_models.register_custom_configs()
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def init_model(model_name: str) -> PreTrainedModel:
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config = AutoConfig.for_model(model_type=model_name)
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if model_name in custom_models.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
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model = custom_models.AutoModelForCausalLM.from_config(config)
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elif model_name in custom_models.MODEL_MAPPING_NAMES:
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model = custom_models.AutoModel.from_config(config)
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else:
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try:
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model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
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except ValueError:
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model = AutoModel.from_config(config, trust_remote_code=True)
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return model
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def load_model(model_name_or_path: Union[str, os.PathLike]) -> PreTrainedModel:
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config = AutoConfig.from_pretrained(model_name_or_path)
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if config.model_type in custom_models.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
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model = custom_models.AutoModelForCausalLM.from_pretrained(model_name_or_path, config=config)
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elif config.model_type in custom_models.MODEL_MAPPING_NAMES:
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model = custom_models.AutoModel.from_pretrained(model_name_or_path, config=config)
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else:
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try:
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, config=config, trust_remote_code=True)
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except ValueError:
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model = AutoModel.from_pretrained(model_name_or_path, config=config, trust_remote_code=True)
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return model
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def load_tokenizer(tokenizer_name_or_path: Union[str, os.PathLike]) -> PreTrainedTokenizer:
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, padding_side='left', trust_remote_code=True)
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token = tokenizer.eos_token
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return tokenizer
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