Worked on the together-python repository to enhance the robustness of the fine-tuning checkpoint download process. Focused on backend development using Python, the work addressed a bug where an incorrect query parameter was used during checkpoint downloads for full training types. By correcting the parameter to model_output_path, the solution ensured accurate identification and retrieval of checkpoints, preventing mislabeling and download errors. The approach emphasized careful debugging, parameter validation, and clear version control practices, resulting in a well-documented and traceable code fix. This targeted update improved the reliability of the fine-tuning workflow and maintained consistency across training runs.