# load_model.py

import torch

# ... (import your model definition and ModelArgs from model.py and model_args.py)

def main():
    args = ModelArgs()
    model = CombinedMultiModalTransformer(args)

    # Load the entire model
    model = torch.load('my_model.pth')

    # Alternatively, load the model's state dictionary
    # checkpoint = torch.load('my_model_state_dict.pth')
    # model.load_state_dict(checkpoint['model_state_dict'])  # Assuming you saved it as 'model_state_dict'

    # Move the model to the appropriate device (optional)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    # Set the model to evaluation mode (if you're not training)
    model.eval()

    # ... (use the loaded model for inference)

if __name__ == "__main__":
    main()