Subsequently, we analyze the difference between the original model and the newly retrained version to verify changes in implementation did not result in significant changes in baselines. The results are shown in Table . Firstly, we observed no significant difference between accuracy scores, and only slight deviations in invalid and unique values. However, a significant difference was observed in the carbon footprints, which can be mostly attributed to the loading of model weights of the original model over inference after training. Additionally, models were also trained using lowest validation loss, which showed lower top-1 accuracy but increased top-10 accuracy in all randomly initialized and fine-tuned models (Table ). The highest change was the Chemformer (FT-Zinc) which went from 53.6 top-1 and 61.7 top-10 accuracy to 48.3 top-1 and 79.7 top-10 accuracy, for the last and optimal validation epoch, respectively. In this setting, the optimized models showed minimal differences or even improvements with respect to the randomly initialized smaller models, all reaching above 79% top-10 accuracy.