Developed the ModelRunner DataKit module for the pytorch/executorch repository, focusing on enhancing machine learning model execution reliability and maintainability. The work introduced structured runtime error handling, standardized value representations, and robust tensor operations, all implemented using Swift and leveraging machine learning expertise. By refactoring and relocating ModelDataKit into the ExecuTorch directory, the codebase became more organized and easier to maintain, supporting scalable model execution workflows. No critical bugs were addressed during this period, as the primary emphasis was on delivering new features and improving repository structure to streamline future development and facilitate efficient, debuggable model runtime operations.