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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "kz919/QwQ-0.5B-Distilled-SFT" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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@spaces.GPU |
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def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p): |
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prompt = system_message + "\n" |
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for user_input, assistant_response in history: |
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prompt += f"User: {user_input}\nAssistant: {assistant_response}\n" |
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prompt += f"User: {message}\nAssistant:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate( |
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inputs.input_ids, |
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max_length=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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pad_token_id=tokenizer.eos_token_id, |
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) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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yield response.split("Assistant:")[-1].strip() |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step.", label="System message"), |
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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() |
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