mirror of https://github.com/THUDM/ChatGLM-6B
35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from langchain.llms.base import LLM
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from typing import Optional, List, Mapping, Any
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from langchain.llms.utils import enforce_stop_tokens
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from transformers import AutoTokenizer, AutoModel
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"""ChatGLM_G is a wrapper around the ChatGLM model to fit LangChain framework. May not be an optimal implementation"""
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class ChatGLM_G(LLM):
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
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history = []
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@property
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def _llm_type(self) -> str:
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return "ChatGLM_G"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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response, updated_history = self.model.chat(self.tokenizer, prompt, history=self.history)
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print("ChatGLM: prompt: ", prompt)
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print("ChatGLM: response: ", response)
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if stop is not None:
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response = enforce_stop_tokens(response, stop)
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self.history = updated_history
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return response
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def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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response, updated_history = self.model.chat(self.tokenizer, prompt, history=self.history)
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print("ChatGLM: prompt: ", prompt)
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print("ChatGLM: response: ", response)
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if stop is not None:
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response = enforce_stop_tokens(response, stop)
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self.history = updated_history
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return response |