Developed a Safetensors Loading Validation and Robustness feature for the ml-explore/mlx repository, focusing on improving data integrity and reliability in machine learning pipelines. The work involved validating data offsets against file boundaries to prevent out-of-bounds reads, thereby reducing runtime errors during file loading. Robust error handling was implemented to address invalid data offsets, ensuring that the loader fails gracefully rather than causing production issues. This feature was delivered using C++ with an emphasis on error handling, file I/O, and unit testing. The contribution reflected collaborative development practices and enhanced the overall robustness of safetensors loading in the repository.