During a two-month period, Liguodong contributed to liguodongiot/transformers by developing and documenting the Universal Checkpointing feature for DeepSpeed, focusing on maintainability and user guidance for resuming long-running model training. The work involved writing comprehensive Markdown documentation and Python examples to improve onboarding and knowledge transfer. In the huggingface/torchtitan repository, Liguodong addressed a critical edge-case in the learning rate scheduler by fixing a ZeroDivisionError when decay_steps was set to zero, ensuring stability in production training workflows. The contributions demonstrated strong skills in Python, data science, and model training, with careful attention to code quality and repository standards.