When analyzing the randomly initialized Transformer models trained on USPTO-50k, we compared each model with baseline parameters to the model with optimal hyper-parameters in terms of accuracy and RCS. When optimizing for accuracy, the resulting model had around 25% of the original size with only a 1.4% decrease in top-1 accuracy, coupled with a 5% increase in top-10 accuracy (Table ). Optimizing on the RCS instead resulted in a model 10% of the original size with a 5% drop in top-1 accuracy and a 16% increase in top-10 accuracy. Notable, even though the sampling time dropped from 9.67 s to 6.52 s between optimizing on accuracy instead of RCS, the carbon footprint was slightly higher for the RCS-optimized model (0.40 g instead of 0.34 g). However, both were substantially lower than the original implementation, indicating that carbon footprints decreased in these optimized models.