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from transformers import AutoTokenizer |
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import transformers |
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import os |
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import sys |
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import fire |
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import torch |
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import gradio as gr |
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def main( |
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base_model="ise-uiuc/Magicoder-S-DS-6.7B", |
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port=8080, |
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): |
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=base_model, |
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torch_dtype=torch.float16, |
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device=device |
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) |
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def evaluate_magicoder( |
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instruction, |
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temperature=1, |
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max_new_tokens=2048, |
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): |
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MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. |
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@@ Instruction |
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{instruction} |
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@@ Response |
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""" |
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prompt = MAGICODER_PROMPT.format(instruction=instruction) |
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sequences = pipeline( |
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prompt, |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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) |
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for seq in sequences: |
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print('==========================question=============================') |
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print(prompt) |
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generated_text = seq['generated_text'].replace(prompt, "") |
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print('===========================answer=============================') |
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print(generated_text) |
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return generated_text |
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gr.Interface( |
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fn=evaluate_magicoder, |
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inputs=[ |
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gr.components.Textbox( |
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lines=3, label="Instruction", placeholder="Anything you want to ask Magicoder ?" |
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), |
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gr.components.Slider(minimum=0, maximum=1, value=0, label="Temperature"), |
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gr.components.Slider( |
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minimum=1, maximum=2048, step=1, value=128, label="Max tokens" |
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), |
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], |
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outputs=[ |
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gr.components.Textbox( |
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lines=30, |
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label="Output", |
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) |
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], |
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title="Magicoder", |
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description="This is a LLM playground for Magicoder! Follow us on Github: https://github.com/ise-uiuc/magicoder and Huggingface: https://huggingface.co/ise-uiuc." |
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).queue().launch(share=True, server_port=port) |
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if __name__ == "__main__": |
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fire.Fire(main) |
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