Worked on NVIDIA/Megatron-LM to enhance Transformer configuration validation for mixed dense and Mixture-of-Experts (MoE) setups, addressing runtime errors that previously affected large-model training and inference. Improved the validation logic in Python to ensure stability when deploying MoE configurations, reducing incidents during production workloads. Collaborated across teams to review and refine the solution, focusing on deep learning and machine learning best practices. The targeted fix strengthened the reliability of Megatron-LM by preventing misconfigurations, supporting robust model configuration management. This work demonstrated attention to code quality and effective cross-functional communication, contributing to more stable and maintainable large-scale model deployments.