Worked on targeted optimization of the Qwen-Image model within the vllm-project/vllm-omni repository, focusing on improving deployment efficiency. The main engineering effort involved removing the unused vision tower from the text encoder, which reduced memory consumption and enhanced inference performance. This change was implemented in Python and leveraged deep learning and model optimization skills to streamline the model architecture. The work addressed specific hardware and deployment constraints, laying groundwork for future optimizations at both the hardware and model levels. No bug fixes were recorded during this period, with efforts concentrated on feature development and performance improvements for machine learning workflows.