Worked on stabilizing tensor parallelism in the bytedance-iaas/sglang repository, focusing on improving the reliability and performance of the MiniMaxM2 model in multi-GPU environments. Addressed a critical bug that caused repeated outputs when using tensor parallelism with 16 threads by refining initialization and weight loading logic. This adjustment reduced output nondeterminism and ensured stable inference across distributed setups. Collaborated effectively through co-authored commits and clear documentation, supporting future maintenance and traceability. Leveraged deep learning and model optimization expertise, utilizing Python and PyTorch to enhance the robustness of tensor parallel configurations and deliver more consistent model behavior in production scenarios.