Worked on the NVIDIA-NeMo/Megatron-Bridge repository to deliver integration of Hugging Face model weights with Megatron checkpoints, enabling users to save and load Hugging Face weights alongside native Megatron formats. This feature was implemented in Python and focused on deep learning workflows, leveraging skills in checkpointing and model exporting. The approach introduced configurable options for managing interoperability, allowing seamless migration of models between the Hugging Face and Megatron ecosystems. By aligning with Hugging Face requirements and emphasizing reproducibility, the work reduced friction for users adopting new workflows and broadened the repository’s utility for machine learning practitioners seeking cross-ecosystem compatibility.