Awni Hannun integrated the Bailing Moe model architecture into the ml-explore/mlx-swift-examples repository, focusing on enhancing language-task performance through new configurations and model definitions. Working primarily in Swift, Awni applied expertise in machine learning and model architecture design to deliver an end-to-end feature that emphasized configurability and code quality. The integration was stable, with no major bugs reported, and provided a foundation for downstream experimentation. This work demonstrated a rapid iteration cycle and a thoughtful approach to supporting advanced model architectures in a Swift-based machine learning environment, addressing the need for flexible, high-quality solutions in language modeling tasks.