Worked on stabilizing model loading workflows for the securefederatedai/openfl repository, focusing on the CIFAR-10 model’s state dictionary handling. Addressed runtime errors by ensuring numpy arrays were reliably converted to PyTorch tensors with correct data types and device placement, which improved compatibility across CPU and GPU environments and different PyTorch versions. This engineering effort enhanced the reliability of model restoration, directly supporting production machine learning pipelines. The work was implemented in Python using PyTorch and machine learning best practices, resulting in a more robust and maintainable codebase for downstream training and deployment scenarios without introducing new features.