During October 2024, Jiaqi Qiu developed a targeted training optimization feature for the pytorch/torchtune repository, focusing on single-device full finetuning scenarios. Leveraging Python and PyTorch, Jiaqi designed and integrated a learning rate scheduler that improves training convergence and reduces wall-clock time, addressing the challenges of efficient experimentation on constrained hardware. The implementation required careful attention to model optimization and deep learning workflow integration, ensuring stability and efficiency throughout the finetuning process. While the work was limited to one feature and did not include bug fixes, it demonstrated depth in technical execution and contributed to torchtune’s efficiency-focused development roadmap.