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import gradio as gr |
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import torch |
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import spaces |
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from transformers import pipeline |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = 'HuggingFaceTB/SmolLM-135M-Instruct' |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name).to(device) |
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@spaces.GPU(duration=120) |
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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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input_text = tokenizer.apply_chat_template(messages, tokenize=False) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p, do_sample=True) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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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(value="You are a friendly Chatbot.", 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=0.6, 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=0.92, |
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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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) |
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if __name__ == "__main__": |
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demo.launch() |
