Kqinami contributed to the pytorch/pytorch repository by clarifying the user-facing documentation for RMSNorm, focusing on the default epsilon values used across different computation types. Using Python and leveraging expertise in machine learning and numerical stability, Kqinami aligned the documentation with the actual implementation, ensuring that the behavior for fp16 and bf16 inputs—computed in fp32—was accurately described. This documentation-only update improved guidance for users, reducing confusion around dtype-dependent epsilon values and distinguishing RMSNorm from LayerNorm and GroupNorm defaults. The work demonstrated careful attention to detail and a strong understanding of both the codebase and user needs, though limited in scope.