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import gradio as gr |
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from huggingface_hub import InferenceClient |
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client = InferenceClient("wop/kosmox") |
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def format_messages(history, user_message): |
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formatted_message = "<s>" |
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for user_msg, assistant_msg in history: |
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if user_msg: |
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formatted_message += f"<|user|>\n{user_msg}\n" |
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if assistant_msg: |
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formatted_message += f"<|assistant|>\n{assistant_msg}\n" |
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formatted_message += f"<|user|>\n{user_message}\n" |
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return formatted_message |
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def respond( |
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message: str, |
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history: list[tuple[str, str]], |
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system_message: str, |
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max_tokens: int, |
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temperature: float, |
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top_p: float, |
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): |
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formatted_message = format_messages(history, message) |
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response = "" |
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for message in client.chat_completion( |
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formatted_message, |
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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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token = message.choices[0].delta.content |
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response += token |
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yield response |
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demo = gr.ChatInterface( |
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fn=respond, |
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additional_inputs=[ |
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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(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |