Langchain-Chatchat/chatglm_llm.py

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from langchain.llms.base import LLM
from typing import Optional, List
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoTokenizer, AutoModel
import torch
DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
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tokenizer = AutoTokenizer.from_pretrained(
"/Users/liuqian/Downloads/ChatGLM-6B/chatglm_hf_model",
# "THUDM/chatglm-6b",
trust_remote_code=True
)
model = (
AutoModel.from_pretrained(
"/Users/liuqian/Downloads/ChatGLM-6B/chatglm_hf_model",
# "THUDM/chatglm-6b",
trust_remote_code=True)
.float()
.to("mps")
# .half()
# .cuda()
)
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class ChatGLM(LLM):
max_token: int = 10000
temperature: float = 0.1
top_p = 0.9
history = []
def __init__(self):
super().__init__()
@property
def _llm_type(self) -> str:
return "ChatGLM"
def _call(self,
prompt: str,
stop: Optional[List[str]] = None) -> str:
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response, updated_history = model.chat(
tokenizer,
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prompt,
history=self.history,
max_length=self.max_token,
temperature=self.temperature,
)
torch_gc()
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print("history: ", self.history)
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = updated_history
return response
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def get_num_tokens(self, text: str) -> int:
tokenized_text = tokenizer.tokenize(text)
return len(tokenized_text)
if __name__ == "__main__":
history = []
while True:
query = input("Input your question 请输入问题:")
resp, history = model.chat(tokenizer,
query,
history=history,
temperature=0.01,
max_length=100000)
print(resp)