During September 2025, Liguodong focused on architectural improvements to the T5Gemma model within the liguodongiot/transformers repository. He delivered a feature that removed split modules from both the encoder and decoder layers, resulting in a more modular and maintainable implementation. This refactoring aimed to streamline the model’s structure, supporting faster iteration and easier deployment. Liguodong applied deep learning and model optimization skills, leveraging Python and PyTorch to ensure code quality and maintainability. While no major bugs were addressed during this period, the work demonstrated depth in modular refactoring and contributed to the long-term scalability of the codebase.