Worked on optimizing the GLM-4.7 model for deployment on NPU hardware within the bytedance-iaas/sglang repository, focusing on compatibility and throughput improvements. Implemented dual-stream processing to efficiently handle both shared and routed streams, addressing the need for scalable and high-performance inference workloads in production environments. Leveraged deep learning and machine learning expertise, utilizing PyTorch and NPU optimization techniques to enhance model readiness for production use. The work resulted in improved performance and scalability for GLM-4.7 deployments, supporting more efficient inference on specialized hardware. No major bugs were addressed during this period, with efforts concentrated on feature development.