Here is the code. Basically it loads langchain llm model.
from typing import List, Optional
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoModel, AutoTokenizer
from config import Config
class LLMService(LLM):
max_token: int = 10000
temperature: float = 0.1
top_p = 0.9
history = []
tokenizer: object = None
model: object = None
def __init__(self):
super().__init__()
@property
def _llm_type(self) -> str:
return "LLM"
def _call(self,
prompt: str,
stop: Optional[List[str]] = None) -> str:
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=self.history,
max_length=self.max_token,
temperature=self.temperature,
)
if stop is not None:
response = enforce_stop_tokens(response, stop)
self.history = self.history + [[None, response]]
return response
def load_model(self, model_name_or_path: str = "ClueAI/ChatYuan-large-v2"):
self.tokenizer = AutoTokenizer.from_pretrained(
Config.llm_model_name,
trust_remote_code=True
)
self.model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
self.model = self.model.eval()
if __name__ == '__main__':
chatLLM = LLMService()
chatLLM.load_model()
Usually, if we want to use DeepSparse on llm, we do it like this,
llm = DeepSparse(
model=MODEL_PATH,
model_config={"sequence_length": 2048, "trust_remote_code": True},
generation_config={"max_new_tokens": 300},
)
But in my case I use transformer. So my question is that how to use DeepSparse in my case to optimize LLMs for CPU inference?