Worked on stabilizing the DeepSeek-V3 weight update workflow in the openanolis/sglang repository, focusing on improving reliability during model weight updates. Addressed a critical issue where the update_weights_from_disk function could crash by ensuring that weight and weight_scale data were correctly assigned to layer parameters without reinitializing Parameter objects. This approach maintained the integrity of existing parameters, reducing the risk of runtime errors and minimizing maintenance overhead. Utilized Python and deep learning techniques, with an emphasis on model optimization, to enhance the stability of production deployments. The work contributed to smoother weight update processes and more dependable model performance in production environments.