Tanayendu Bari focused on stabilizing cross-attention in eager mode for the Habana-backed optimum-habana workflow, specifically addressing a masking bug in the Mllama model. Working in Python and leveraging deep learning and model optimization expertise, Tanayendu implemented a solution that clones the attention mask during index copying, preventing unintended modifications to the original mask. This approach resolved a cross-attention masking issue affecting Llama 3.2 integration, improving the correctness and reliability of production deployments. The fix, contributed directly to the huggingface/optimum-habana repository, enhanced workflow stability and reduced debugging time, demonstrating careful attention to detail and a strong understanding of transformer architectures.