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from fastapi import FastAPI, Request |
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from llama_cpp import Llama |
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from huggingface_hub import hf_hub_download |
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import os |
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import requests |
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app = FastAPI() |
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@app.post("/api") |
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async def completion(request: Request): |
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try: |
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data = await request.json() |
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prompt = data["prompt"] |
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mode = data['mode'] |
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if mode == 'f': |
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return requests.post("https://mrali-codexpert-computing.hf.space/api", json={"prompt": prompt}).json() |
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else: |
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res = model_l( |
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prompt, |
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temperature=0.6, |
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echo=False, |
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max_tokens=32, |
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) |
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return {"responses": res["choices"]} |
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except Exception as j: |
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print(j) |
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return {"responses": "Error!"} |
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|
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if __name__ == "__main__": |
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os.system("ulimit -l unlimited") |
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|
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hf_hub_download("TheBloke/deepseek-coder-1.3b-base-GGUF", "deepseek-coder-1.3b-base.Q5_K_M.gguf", local_dir="./") |
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global model_l |
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|
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model_l = Llama(model_path="./deepseek-coder-1.3b-base.Q5_K_M.gguf", n_ctx=16192, n_gpu_layers=0, n_threads=2, use_mlock=True) |
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|
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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