Worked on the liguodongiot/transformers repository to deliver a focused architectural enhancement for the T5Gemma model. The primary contribution involved removing split modules from both encoder and decoder layers, resulting in a no-split modular design that improves code maintainability and supports more efficient model iteration. This refactoring aimed to streamline the model’s structure, making it easier to deploy and extend in future development cycles. The work was implemented using Python and PyTorch, leveraging deep learning and model optimization expertise. Efforts during this period centered on feature delivery and code quality, with no major bug fixes addressed in the same timeframe.