During January 2026, Jeejee Lee enhanced platform compatibility for the KimiK25 model in the jeejeelee/vllm repository by refactoring device selection logic. By replacing torch.cuda.current_device() with a platform-agnostic current_platform.current_device(), Lee enabled smoother deployment across both CUDA and non-CUDA environments. This Python-based change reduced platform-specific edge cases and prepared the codebase for future platform-agnostic improvements. The work demonstrated practical application of deep learning and PyTorch skills, with careful attention to maintainability through Git-backed backporting. While the contribution focused on a single feature, it addressed a core deployment challenge and laid groundwork for broader cross-platform support.