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import os |
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import gradio as gr |
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import copy |
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from llama_cpp import Llama |
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from huggingface_hub import hf_hub_download |
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llm = Llama( |
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model_path=hf_hub_download( |
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repo_id=os.environ.get("REPO_ID", "Lyte/Llama-3.1-8B-Instruct-Reasoner-1o1_v0.3"), |
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filename=os.environ.get("MODEL_FILE", "unsloth.Q4_K_M.gguf"), |
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), |
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n_ctx=4096, |
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n_gpu_layers=-1, |
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) |
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training_prompt = """<|start_header_id|>system<|end_header_id|> |
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You are a world-class AI system, capable of complex reasoning and reflection and correcting your mistakes. Reason through the query/question, and then provide your final response. If you detect that you made a mistake in your reasoning at any point, correct yourself.<|eot_id|><|start_header_id|>user<|end_header_id|> |
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{}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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{}""" |
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EOS_TOKEN = "<|eot_id|>" |
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def generate_text( |
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message, |
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history: list[tuple[str, str]], |
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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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temp = "" |
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input_prompt = "" |
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for user_input, assistant_response in history: |
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input_prompt += training_prompt.format(user_input, assistant_response) |
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input_prompt += training_prompt.format(message, "") |
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output = llm( |
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input_prompt, |
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temperature=temperature, |
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top_p=top_p, |
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top_k=40, |
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repeat_penalty=1.1, |
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max_tokens=max_tokens, |
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stop=[ |
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EOS_TOKEN, |
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"<|endoftext|>" |
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], |
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stream=True, |
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) |
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for out in output: |
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stream = copy.deepcopy(out) |
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temp += stream["choices"][0]["text"] |
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yield temp |
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demo = gr.ChatInterface( |
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generate_text, |
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title="Llama-3.1-8B-Instruct-Reasoner", |
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description="Running LLM with https://github.com/abetlen/llama-cpp-python", |
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examples=[ |
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['How to setup a human base on Mars? Give short answer.'], |
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['Explain theory of relativity to me like I’m 8 years old.'], |
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['What is 9,000 * 9,000?'], |
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['Write a pun-filled happy birthday message to my friend Alex.'], |
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['Justify why a penguin might make a good king of the jungle.'] |
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], |
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cache_examples=False, |
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retry_btn=None, |
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undo_btn="Delete Previous", |
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clear_btn="Clear", |
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additional_inputs=[ |
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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() |