Developed and delivered the GDN-2 model introduction for the flash-linear-attention repository, focusing on deep learning and GPU programming with Python and PyTorch. The work introduced independent channel-wise erase and write gates, new Triton-based operations, and a dedicated GDN-2 layer, enabling chunkwise training and token-by-token inference. Comprehensive tests were implemented to validate correctness and stability, including explicit gate-count validation and expanded coverage across multiple feature combinations. Documentation and CI lint issues were addressed to improve maintainability. The end-to-end GDN-2 flow was validated on NVIDIA Turing and T4 GPUs, supporting both FP16 and FP32 precision for robust deployment scenarios.