
# model_management.py
from transformers import AutoModel, AutoTokenizer

def download_model(model_name: str, save_path: str):
    """
    Downloads a pre-trained model and tokenizer.
    """
    model = AutoModel.from_pretrained(model_name, cache_dir=save_path)
    tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=save_path)
    return model, tokenizer

def fine_tune_model(model, dataset_path: str, output_path: str, epochs: int):
    """
    Fine-tunes the model on a custom dataset.
    """
    # Placeholder for fine-tuning logic
    print(f"Fine-tuning {model} on {dataset_path} for {epochs} epochs...")
    # Save the fine-tuned model
    model.save_pretrained(output_path)
