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Add application file
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app.py
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import os
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import gradio as gr
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import torch
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import numpy as np
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from transformers import pipeline
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examples = [['Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.'],['Translate to German: My name is Arthur'], ['Please answer the following question. What is the boiling point of Nitrogen?']]
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print(f"Is CUDA available: {torch.cuda.is_available()}")
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print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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#pipe_biogpt = pipeline("text-generation", model="microsoft/BioGPT-Large", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
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pipe_flan_t5 = pipeline("text-generation", model="google/flan-t5-xxl", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
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#pipe_gpt2 = pipeline("text-generation", model="gpt2", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
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pipe_flan_ul2 = pipeline("text-generation", model="google/flan-ul2", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
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#pipe_galactica = pipeline("text-generation", model="facebook/galactica-1.3b", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})
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title = "LLM vs LLM"
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description = "**Disclaimer:** this demo was made for research purposes."
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def inference(text):
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#output_biogpt = pipe_biogpt(text, max_length=100)[0]["generated_text"]
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output_flan_t5 = pipe_flan_t5(text, max_length=100)[0]["generated_text"]
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#output_gpt2 = pipe_gpt2(text, max_length=100)[0]["generated_text"]
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pipe_flan_ul2 = pipe_flan_t5(text, max_length=100)[0]["generated_text"]
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#output_galactica = pipe_galactica(text, max_length=100)[0]["generated_text"]
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return [
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#output_biogpt,
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output_flan_t5,
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#output_gpt2,
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pipe_flan_ul2,
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#output_galactica
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]
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io = gr.Interface(
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inference,
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gr.Textbox(lines=3),
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outputs=[
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#gr.Textbox(lines=3, label="Microsoft: BioGPT-Large"),
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gr.Textbox(lines=3, label="Google: FLAN-T5-XXL"),
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#gr.Textbox(lines=3, label="GPT-2"),
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gr.Textbox(lines=3, label="Google: FLAN-UL2"),
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#gr.Textbox(lines=3, label="Facebook: Galactica 1.3B"),
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],
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title=title,
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description=description,
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examples=examples
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)
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io.launch()
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