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from langchain.llms import CTransformers
from fastapi import FastAPI
from pydantic import BaseModel
file_name = "zephyr-7b-beta.Q4_K_S.gguf"
config = {
"max_new_tokens": 1024,
"model_type": "mistral",
# "stream": True,
}
llm = CTransformers(
model=file_name,
**config
)
class validation(BaseModel):
prompt: str
#Fast API
app = FastAPI()
@app.post("/llm_on_cpu")
async def stream(item: validation):
system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.'
E_INST = "</s>"
user, assistant = "<|user|>", "<|assistant|>"
prompt = f"{system_prompt}{E_INST}\n{user}\n{item.prompt}{E_INST}\n{assistant}\n"
return llm(prompt)
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