Worked on the jd-opensource/xllm repository to deliver the DeepSeek-V3.2 MTP model optimized for NPU integration, focusing on high-performance, on-device inference. The engineering effort involved implementing advanced attention mechanisms, handling variable sequence lengths, and modifying decoder layers to suit NPU architecture. A new MTP header file was created and integrated to streamline deployment and support future feature expansion. Collaboration with other contributors ensured smooth cross-team integration and code quality. The work was carried out using C++ and leveraged expertise in NPU programming, deep learning, and machine learning, establishing a robust foundation for production-ready, efficient model deployment.