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from ctransformers import AutoModelForCausalLM |
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from fastapi import FastAPI |
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from pydantic import BaseModel |
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llm = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-7B-Instruct-GGUF", |
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model_file="codellama-7b-instruct.q4_K_M.gguf", |
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model_type="llama", |
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gpu_layers=0) |
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class validation(BaseModel): |
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prompt: str |
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app = FastAPI() |
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@app.post("/llm_on_cpu") |
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async def stream(item: validation): |
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system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' |
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E_INST = "</s>" |
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user, assistant = "<|user|>", "<|assistant|>" |
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prompt = f"{system_prompt}{E_INST}\n{user}\n{item.prompt}{E_INST}\n{assistant}\n" |
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return llm(prompt) |