app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

# Configuração da quantização
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,  # ou use True para 4-bit
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

# Inicializa o modelo e tokenizer
model_name = "Orenguteng/Llama-3-8B-Lexi-Uncensored"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    quantization_config=quantization_config
)

def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs["input_ids"],
        max_new_tokens=100,
        temperature=0.7,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Cria a interface
iface = gr.Interface(
    fn=generate_text,
    inputs="text",
    outputs="text",
    title="LLama Chat"
)

iface.launch()