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# app.py
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load the model and tokenizer
adapter_model_name = "acezxn/SOC_Task_Generation_Base"
base_model_name = "unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit" # You may want to change this to a standard model name
# Load the tokenizer and model for CPU use (no bitsandbytes)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
# Load model for CPU usage without 4-bit quantization
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
# Do not use bitsandbytes for quantization, just use the normal model
load_in_4bit=False, # Ensure not using 4-bit quantization
device_map=None, # Use CPU (no device mapping needed)
trust_remote_code=True # If necessary for running with remote code
)
# Move model to CPU (explicit, but optional)
base_model.to('cpu')
# Load the LoRA adapter
adapter_model = PeftModel.from_pretrained(base_model, adapter_model_name)
# Function to generate a response using the model
def generate_response(input_text):
inputs = tokenizer(input_text, return_tensors="pt").to('cpu') # Ensure inputs are on CPU
outputs = adapter_model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create Gradio interface
iface = gr.Interface(fn=generate_response,
inputs=gr.Textbox(lines=2, placeholder="Enter your input here..."),
outputs=gr.Textbox(),
title="Llama LORA Adapter - SOC Task Generation",
description="This is a Gradio app that uses a Llama LORA adapter (acezxn/SOC_Task_Generation_Base) with the base model Llama-3.2-3B-Instruct-unsloth-bnb-4bit to generate task-related responses.")
# Launch the interface
if __name__ == "__main__":
iface.launch()
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