Developed multimodal support for Qwen3.5 within the linkedin/Liger-Kernel repository, enabling the model’s forward function to process both text and image inputs for conditional generation tasks. The work involved implementing a dedicated multimodal forward path, introducing a new output type to handle multimodal results, and expanding the test suite to cover these workflows. All changes were validated end-to-end on NVIDIA A100 hardware, ensuring robustness through unit tests, style checks, and convergence tests. Leveraged Python and deep learning frameworks to extend model development capabilities, focusing on natural language processing and machine learning techniques to broaden the model’s applicability and reliability.