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 model structure. This refactoring aimed to streamline the codebase, support faster iteration, and facilitate easier deployment. Liguodong applied his expertise in Python, PyTorch, and deep learning model optimization throughout the process. While no major bugs were addressed during this period, his work demonstrated a thoughtful approach to code quality and maintainability, emphasizing long-term benefits for the repository’s development.