Worked on the jeejeelee/vllm repository to enhance the robustness of multimodal inference by implementing hidden-dimension validation for embedding inputs. This solution addressed a recurring issue where mismatched input dimensions could cause inference crashes, thereby improving the stability of production deployments. The approach involved adding targeted data validation logic using Python, with careful attention to unit testing and adherence to established code-review practices. By focusing on API development and machine learning workflows, the work ensured safer and more reliable multimodal processing. The change was delivered as a focused bug fix, supporting maintainable and auditable code in a collaborative development environment.