Worked on enhancing model configuration robustness in the microsoft/onnxruntime-genai repository by addressing a critical validation gap in the configuration process. Implemented a non-null check for the head_dim parameter within builder.py, ensuring accurate head_size calculation and preventing configuration-time errors that could disrupt GenAI deployments. This targeted bug fix improved the stability and maintainability of the codebase by introducing explicit input validation, thereby reducing debugging time for both developers and end users. Leveraged Python development skills and a strong understanding of machine learning model configuration to deliver a focused solution that increased reliability for complex AI model deployment scenarios.