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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
from transformers import pipeline
import torch
hf_token = os.getenv('HF_API_TOKEN')
# Load the Llama 3.1 model and tokenizer
model_name = "meta-llama/Meta-Llama-3.1-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name, token= hf_token)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", token= hf_token)
# Streamlit app interface
st.title("Llama 3.1 Text Generator")
prompt = st.text_area("Enter a prompt:", "Once upon a time")
if st.button("Generate"):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=512, top_p=0.9, temperature=0.8)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(generated_text)
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