Worked on the pytorch/pytorch repository to enhance test coverage and reliability for quantization of MobileNet V2 and V3 models on ARM64 platforms. Focused on restoring and re-enabling previously skipped quantization tests, addressing gaps in quality assurance for mobile model deployment. Used Python to validate and expand the quantization test suite, ensuring consistent pass rates and improved CI stability. Applied skills in machine learning, quantization, and unit testing to resolve a key bug, directly supporting PyTorch’s ongoing efforts to maintain robust support for AArch64 architectures. The work contributed to more reliable and comprehensive quantization functionality for mobile inference scenarios.