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import os |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from llama_cpp import Llama |
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import spaces |
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huggingface_token = os.getenv("HF_TOKEN") |
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infer_prompt = "فيما يلي تعليمات تصف مهمة. اكتب استجابة تكمل الطلب بشكل مناسب.\n\n### تعليمات:\n{}\n\n### إجابة:\n" |
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model_id = "nazimali/mistral-7b-v0.3-instruct-arabic" |
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file_name = "Q8_0.gguf" |
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llm = None |
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hf_hub_download( |
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repo_id=model_id, |
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filename=file_name, |
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local_dir="./models", |
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token=huggingface_token, |
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) |
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@spaces.GPU |
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def respond( |
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message, |
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history, |
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): |
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global llm |
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if llm is None: |
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llm = Llama( |
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model_path=f"./models/{file_name}", |
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flash_attn=True, |
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n_gpu_layers=-1, |
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n_ctx=2048, |
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verbose=True, |
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) |
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stream = llm.create_chat_completion( |
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messages=[{"role": "user", "content": infer_prompt.format(message) }], |
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max_tokens=50, |
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repeat_penalty=1.2, |
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stream=True, |
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temperature=0.7, |
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top_k=40, |
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top_p=0.95, |
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) |
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outputs = "" |
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for output in stream: |
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print(output) |
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outputs += output["choices"][0]["delta"].get("content", "") |
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yield outputs |
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demo = gr.ChatInterface(respond, examples=["السلام عليكم", "hello"], title="Mistral 7B Arabic Fine-tuned") |
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if __name__ == "__main__": |
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demo.launch() |
