model.py

# model.py
import torch
import os
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel, LoraConfig, TaskType

def load_t5_qa_model(base_model_name, adapter_path, device="cpu"):
    print("Loading T5 QA tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(base_model_name)

    print(f"Loading base T5 model: {base_model_name}...")
    base_model = AutoModelForSeq2SeqLM.from_pretrained(base_model_name)

    print(f"Loading PEFT adapter from {adapter_path} and merging...")
    if not os.path.exists(adapter_path):
        raise FileNotFoundError(f"Adapter path not found: {adapter_path}")

    peft_config = LoraConfig(
        task_type=TaskType.SEQ_2_SEQ_LM,
        inference_mode=False,
        r=8,
        lora_alpha=32,
        lora_dropout=0.1
    )

    model = PeftModel.from_pretrained(base_model, adapter_path)
    model = model.merge_and_unload()
    model.eval()
    model.to(device)

    print(f"Model loaded and moved to {device}.")
    return tokenizer, model