Contributed to the NVIDIA/Megatron-LM repository by implementing YARN Rotary Embedding support within the HybridModel, enabling advanced positional embeddings for improved long-context modeling and greater experimental flexibility. The work involved developing the YarnRotaryEmbedding module using PyTorch, updating model configuration to integrate the new embedding, and applying test-driven development practices to ensure robust feature delivery. Unit tests were added to validate the embedding functionality and protect against regressions, reflecting a focus on code quality and maintainability. Collaboration was documented through co-authored commits, and the work demonstrated skills in natural language processing, deep learning, and Python-based model engineering.