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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr
# Load ultralight model
model_id = "sshleifer/tiny-t5"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
# Simple chat function
def chat(message, history):
prompt = "Answer this: " + message
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response.strip()
# Gradio chat interface
gr.ChatInterface(
fn=chat,
title="TinyT5 Chat",
description="Ask basic questions. Powered by the super lightweight sshleifer/tiny-t5 model.",
).launch()
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