| from fastapi import FastAPI, HTTPException |
| from pydantic import BaseModel |
| from typing import List, Optional |
| from huggingface_hub import hf_hub_download |
| from llama_cpp import Llama |
| import uvicorn |
|
|
| app = FastAPI(title="ChemLLM CPU OpenAI API") |
|
|
| print("Завантаження GGUF моделі...") |
| model_path = hf_hub_download( |
| repo_id="RichardErkhov/AI4Chem___ChemLLM-7B-Chat-1_5-DPO-gguf", |
| filename="ChemLLM-7B-Chat-1_5-DPO.Q4_K_M.gguf" |
| ) |
|
|
| llm = Llama(model_path=model_path, n_ctx=2048, n_threads=4) |
| print("Модель успішно завантажена на CPU!") |
|
|
| class ChatMessage(BaseModel): |
| role: str |
| content: str |
|
|
| class ChatCompletionRequest(BaseModel): |
| model: str |
| messages: List[ChatMessage] |
| temperature: Optional[float] = 0.7 |
| max_tokens: Optional[int] = 256 |
|
|
| @app.post("/v1/chat/completions") |
| async def chat_completions(request: ChatCompletionRequest): |
| try: |
| full_prompt = "" |
| for msg in request.messages: |
| if msg.role == "user": |
| full_prompt += f"<|User|>:{msg.content}" |
| elif msg.role == "assistant": |
| full_prompt += f"<|Bot|>:{msg.content}" |
| full_prompt += "<|Bot|>:" |
|
|
| |
| output = llm( |
| full_prompt, |
| max_tokens=request.max_tokens, |
| temperature=request.temperature, |
| stop=["<|User|>", "<|Bot|>", "\n\n"] |
| ) |
| |
| response_text = output["choices"][0]["text"].strip() |
|
|
| return { |
| "id": "chatcmpl-chem-cpu", |
| "object": "chat.completion", |
| "model": request.model, |
| "choices": [{ |
| "index": 0, |
| "message": { |
| "role": "assistant", |
| "content": response_text |
| }, |
| "finish_reason": "stop" |
| }] |
| } |
| except Exception as e: |
| raise HTTPException(status_code=500, detail=str(e)) |
|
|
| @app.get("/") |
| def health(): |
| return {"status": "healthy", "hardware": "CPU"} |
|
|
| if __name__ == "__main__": |
| uvicorn.run(app, host="0.0.0.0", port=7860) |
