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Update app.py
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app.py
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import gradio as gr
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from huggingface_hub import InferenceClient
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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("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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@@ -35,29 +31,18 @@ def respond(
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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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"""
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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
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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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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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client = InferenceClient("TheBloke/LLaMA-2-13B-chat-GPTQ")
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def respond(
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message,
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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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respond,
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additional_inputs=[
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gr.Textbox(value="You are a friendly, empathetic woman who enjoys deep conversations and role-playing.", 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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