In June 2025, J. Chang developed a configurable head_dim parameter for the Attention and Rope modules in the mosaicml/llm-foundry repository. Using Python and PyTorch, Chang enhanced the model architecture by allowing flexible adjustment of attention dimensions, which supports more efficient experimentation and tuning of transformer models. The implementation ensured that head_dim was consistently applied in weight and bias calculations for attention projections, reducing the risk of misconfiguration and enabling scalable architectural customization. This work provided a foundation for more robust and adaptable deep learning workflows, reflecting a focused and technically sound approach to improving model flexibility and experimentation.