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from ctransformers import AutoModelForCausalLM
from fastapi import FastAPI, Form
from pydantic import BaseModel
#Model loading
llm = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-7B-Python-GGUF",
model_file="Meta-Llama-3-8B-Instruct-Q4_K_M.gguf",
model_type='llama',
# max_new_tokens = 1096,
threads = 3,
)
#Pydantic object
class validation(BaseModel):
prompt: str
#Fast API
app = FastAPI()
#Zephyr completion
@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.'
prompt = f'''
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{item.prompt.strip()}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
'''
return llm(prompt)
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