Qilin Gai developed a Torch Device Abstraction Layer for the FlagOpen/FlagGems repository, introducing the torch_device_fn to replace direct torch.cuda calls. This approach consolidated device-specific logic, enabling the codebase to support both CPU and GPU environments without hardware-specific branching. By leveraging Python and PyTorch, Qilin focused on API design and device abstraction patterns to improve maintainability and testability across different hardware configurations. The work laid the foundation for future cross-device deployment and broader hardware testing, addressing the need for scalable machine learning workflows. Over the month, Qilin delivered this feature, demonstrating depth in GPU programming and codebase refactoring.