import fastapi from fastapi.responses import JSONResponse from time import time #MODEL_PATH = "./qwen1_5-0_5b-chat-q4_0.gguf" #"./qwen1_5-0_5b-chat-q4_0.gguf" import logging import llama_cpp import llama_cpp.llama_tokenizer llama = llama_cpp.Llama.from_pretrained( repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", filename="*q4_0.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B"), verbose=False, n_ctx=4096, n_gpu_layers=0, chat_format="llama-2" ) # Logger setup logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize Llama model """ try: llm = Llama.from_pretrained( repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", filename="*q4_0.gguf", verbose=False, n_ctx=4096, n_threads=4, n_gpu_layers=0, ) llm = Llama( model_path=MODEL_PATH, chat_format="llama-2", n_ctx=4096, n_threads=8, n_gpu_layers=0, ) except Exception as e: logger.error(f"Failed to load model: {e}") raise """ app = fastapi.FastAPI() @app.get("/") def index(): return fastapi.responses.RedirectResponse(url="/docs") @app.get("/health") def health(): return {"status": "ok"} # Chat Completion API @app.get("/generate") async def complete( question: str, system: str = "You are a story writing assistant.", temperature: float = 0.7, seed: int = 42, ) -> dict: try: st = time() output = llama.create_chat_completion( messages=[ {"role": "system", "content": system}, {"role": "user", "content": question}, ], temperature=temperature, seed=seed, stream=True ) """ for chunk in output: delta = chunk['choices'][0]['delta'] if 'role' in delta: print(delta['role'], end=': ') elif 'content' in delta: print(delta['content'], end='') print(chunk) """ et = time() output["time"] = et - st return output except Exception as e: logger.error(f"Error in /complete endpoint: {e}") return JSONResponse( status_code=500, content={"message": "Internal Server Error"} ) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)