Create llm_for_langchain.py
Browse files- llm_for_langchain.py +56 -0
llm_for_langchain.py
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from langchain.llms.base import LLM
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from typing import Dict, List, Any, Optional
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import torch,sys,os
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from transformers import AutoTokenizer
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class LLM(LLM):
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max_token: int = 4000
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temperature: float = 0.1
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top_p: float = 0.95
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tokenizer: Any
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model: Any
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def __init__(self, model_name_or_path, bit4=True):
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super().__init__()
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self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,use_fast=False)
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self.tokenizer.pad_token = self.tokenizer.eos_token
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if bit4==False:
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from transformers import AutoModelForCausalLM
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self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path,device_map='auto',torch_dtype=torch.bfloat16,load_in_8bit=True)
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self.model.eval()
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else:
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from transformers import BitsAndBytesConfig, AutoModelForCausalLM
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double_quant_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
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self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map='auto', quantization_config=double_quant_config)
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self.model.eval()
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if torch.__version__ >= "2" and sys.platform != "win32":
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self.model = torch.compile(self.model)
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@property
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def _llm_type(self) -> str:
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return "Mistral"
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def _call(self, prompt: str, stop: Optional[List[str]] = None, return_only_outputs = True) -> str:
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print('prompt:',prompt)
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input_ids = self.tokenizer(prompt, return_tensors="pt",add_special_tokens=False).input_ids.to('cuda')
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generate_input = {
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"input_ids":input_ids,
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"max_new_tokens":1024,
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"do_sample":True,
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"top_k":50,
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"top_p":self.top_p,
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"temperature":self.temperature,
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"repetition_penalty":1.2,
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"eos_token_id":self.tokenizer.eos_token_id,
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"bos_token_id":self.tokenizer.bos_token_id,
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"pad_token_id":self.tokenizer.pad_token_id
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}
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generate_ids = self.model.generate(**generate_input)
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generate_ids = [item[len(input_ids[0]):-1] for item in generate_ids]
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result_message = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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return result_message
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