Large: 6 encoder and decoder layers, 16 attention heads Each configuration was trained on spectral data from 1 million molecules from the ZINC dataset for 20 epochs, using Cross Entropy loss for SMILES prediction which allowed a direct comparison of architectural complexity under controlled conditions. We also investigated the impact of training dataset size on model performance. Datasets of 100k, 1M, 2M, and 4M molecules were prepared, with training epochs adjusted to maintain a constant exposure of 20 million molecules: