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59 lines
1.8 KiB
59 lines
1.8 KiB
from typing import Any, List, Mapping, Optional
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from transformers import AutoTokenizer, AutoModel,AutoConfig
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from args import args
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class CustomLLM(LLM):
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model_kwargs: Optional[dict] = None
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pretrained_model_name = args.pretrained_model_name
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top_k = args.topk
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temperature = args.temperature
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device = args.device
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@property
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def _llm_type(self) -> str:
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return "CustomLLM"
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""返回字典，打印出选择的属性"""
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_model_kwargs = self.model_kwargs or {}
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return {
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**{"model_kwargs": _model_kwargs},
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}
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def _call(self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,) -> str:
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_model_kwargs = self.model_kwargs or {}
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payload = {
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"temperature": self.temperature,
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"top_k": self.top_k,
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}
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model_config = AutoConfig.from_pretrained(self.pretrained_model_name, trust_remote_code=True)
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model_config.update(payload)
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model_config.update(_model_kwargs)
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model_config.update(kwargs)
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model = AutoModel.from_pretrained(self.pretrained_model_name, config=model_config, trust_remote_code=True).half().to(
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self.device)
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tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model_name, trust_remote_code=True)
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model = model.eval()
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response, history = model.chat(tokenizer, prompt, history=[])
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if stop is not None:
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response = enforce_stop_tokens(response, stop)
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return response
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