Worked on the oumi-ai/oumi repository to optimize the Gemma3 model’s fine-tuning training configuration, focusing on improving both performance and efficiency during supervised fine-tuning cycles. The approach involved adjusting key hyperparameters such as maximum sequence length, training epochs, and logging steps, with changes informed by offline tuning feedback. Leveraged configuration management skills and YAML to implement and document these updates, ensuring clear traceability through Git-based change tracking. The result was faster fine-tuning cycles, enhanced observability, and potential cost savings, ultimately accelerating model readiness for deployment. No major bugs were addressed during this period, with efforts concentrated on feature delivery.