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import gradio as gr |
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from llama_cpp import Llama |
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model = "Qwen/Qwen2-7B-Instruct-GGUF" |
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llm = Llama.from_pretrained( |
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repo_id=model, |
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filename="qwen2-7b-instruct-q4_k_m.gguf", |
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verbose=True, |
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use_mmap=False, |
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use_mlock=True, |
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n_threads=2, |
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n_threads_batch=2, |
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n_ctx=8000, |
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) |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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completion = llm.create_chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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) |
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for message in completion: |
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print(message) |
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token = message['choices'][0]['delta']['text'] |
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response += token |
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yield response |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox( |
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value="You are a helpful assistant.", |
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label="System message", |
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), |
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gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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description=model, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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