Worked on stabilizing core multimodal utilities within the liguodongiot/transformers repository, focusing on improving the robustness of the Gemma3n model’s placeholder mask generation. Addressed a reliability gap by updating the get_placeholder_mask method to accept None as a valid input for optional parameters, ensuring default values are applied and preventing crashes in multimodal tasks. This change aligned with referenced issues to enhance traceability and maintainability. The work involved Python and deep learning model development, contributing to the reliability of downstream pipelines. The update improved the handling of optional inputs, supporting more flexible and resilient machine learning workflows in the codebase.