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import gradio as gr |
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from llama_cpp import Llama |
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model = "Qwen/Qwen1.5-0.5B-Chat-GGUF" |
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llm = Llama.from_pretrained(repo_id=model, filename="*q8_0.gguf", verbose=True) |
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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 = llm.create_chat_completion( |
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messages=messages, |
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max_tokens=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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) |
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return response |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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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=512, 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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