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import gradio as gr |
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from llama_cpp import Llama |
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from llama_cpp.llama_chat_format import MoondreamChatHandler |
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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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class MyModel: |
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def __init__(self): |
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self.client = None |
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self.current_model = "" |
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def respond( |
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self, |
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message, |
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history: list[tuple[str, str]], |
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model, |
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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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if model != self.current_model or self.current_model is None: |
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model_id, filename = model.split(",") |
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client = Llama.from_pretrained( |
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repo_id=model_id.strip(), |
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filename=f"*{filename.strip()}*.gguf", |
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n_ctx=2048, |
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) |
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self.client = client |
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self.current_model = model |
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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 self.client.create_chat_completion( |
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messages, |
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temperature=temperature, |
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top_p=top_p, |
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stream=True, |
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max_tokens=max_tokens, |
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): |
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delta = message["choices"][0]["delta"] |
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if "content" in delta: |
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response += delta["content"] |
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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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my_model = MyModel() |
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model_choices = [ |
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"lab2-as/lora_model_gguf, Q4", |
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"lab2-as/lora_model_no_quant_gguf, Q4" |
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] |
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demo = gr.ChatInterface( |
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my_model.respond, |
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additional_inputs=[ |
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gr.Dropdown( |
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choices=model_choices, |
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value=model_choices[0], |
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label="Select Model", |
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), |
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gr.Textbox( |
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value="You are a friendly Chatbot.", |
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label="System message", |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=2048, |
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value=128, |
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step=1, |
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label="Max new tokens", |
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), |
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gr.Slider( |
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minimum=0.1, |
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maximum=4.0, |
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value=0.7, |
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step=0.1, |
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label="Temperature", |
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), |
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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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