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Parent(s):
60e6fbb
Try using llama_cpp with a ZEUS quant
Browse files- app.py +24 -26
- requirements.txt +2 -1
app.py
CHANGED
@@ -1,10 +1,12 @@
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import gradio as gr
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from
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""
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def respond(
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response = ""
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for message in
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messages,
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max_tokens=max_tokens,
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stream=True,
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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 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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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from llama_cpp import Llama
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LLM = Llama.from_pretrained(
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repo_id="mradermacher/ZEUS-8B-V2-i1-GGUF",
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filename="*Q4_K_M.gguf",
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chat_format="llama-2",
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verbose=False
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)
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def respond(
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response = ""
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for message in llm.create_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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yield response
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if __name__ == "__main__":
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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 assistant.", 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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demo.launch()
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requirements.txt
CHANGED
@@ -1 +1,2 @@
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huggingface_hub==0.25.2
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huggingface_hub==0.25.2
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llama_cpp
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