app.py

import os
import gradio as gr
import torch
import numpy as np
from transformers import pipeline

import torch
print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

pipe_flan = pipeline("text2text-generation", model="philschmid/flan-t5-xxl-sharded-fp16", model_kwargs={"load_in_8bit":True, "device_map": "auto"})
pipe_vanilla = pipeline("text2text-generation", model="t5-large", device="cuda:0", model_kwargs={"torch_dtype":torch.bfloat16})

title = "Flan T5 and Vanilla T5"
description = "This demo compares [T5-large](https://huggingface.co/t5-large) and [Flan-T5-XX-large](https://huggingface.co/google/flan-t5-xxl). Note that T5 expects a very specific format of the prompts, so the examples below are not necessarily the best prompts to compare."

def inference(text):
  output_flan = pipe_flan(text, max_length=100)[0]["generated_text"]
  output_vanilla = pipe_vanilla(text, max_length=100)[0]["generated_text"]
  return [output_flan, output_vanilla]

io = gr.Interface(
  inference,
  gr.Textbox(lines=3),
  outputs=[
    gr.Textbox(lines=3, label="Flan T5"),
    gr.Textbox(lines=3, label="T5")
  ],
  title=title,
  description=description,
  examples=examples
)
io.launch()