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import gradio as gr |
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from huggingface_hub import InferenceClient |
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import llama_huggingface |
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import prompts |
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""" |
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference |
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""" |
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client = InferenceClient("meta-llama/Llama-3.2-1B-Instruct") |
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chat_model='' |
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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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for message in client.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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token = message.choices[0].delta.content |
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response += token |
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yield response |
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def respond2( |
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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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prompt=prompts.compose_prompt( |
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system_message='You are a friendly Chatbot.', |
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human_message=message |
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) |
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reply=chat_model.invoke(prompt) |
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yield reply.content |
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def init_gradio(repo_id): |
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global chat_model |
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chat_model=llama_huggingface.init_llama_chatmodel(repo_id=repo_id) |
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def get_gradio_interface(): |
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demo = gr.ChatInterface( |
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respond2, |
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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=0.7, 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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) |
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return demo |
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