Worked on targeted maintenance for the huggingface/torchtitan repository, focusing on improving the reliability and transparency of the model training process. Addressed a bug in the denoising schedule calculation, ensuring accurate timing within the training loop and supporting more reproducible experiments. Enhanced logging messages to provide clearer insights into training runs, which aids in monitoring and accelerates debugging. The work involved Python programming and leveraged expertise in PyTorch, deep learning, and data processing. By refining both the core training logic and its observability, contributed to the maintainability of the torchtitan module and enabled faster diagnosis of issues during machine learning workflows.