Worked on stabilizing RotaryEmbedding usage for chatglm_v2 within the PaddlePaddle/PaddleNLP repository, focusing on resolving a subtle numerical precision issue. Addressed a bug by aligning inv_freq, idx_theta, and cache with Paddle’s default dtype, ensuring consistent data types throughout embedding calculations. Utilized Python and deep learning expertise to implement a maintainable fix that reduced production risk and improved model reliability across deployments. Validated the solution through code review and continuous integration, demonstrating disciplined debugging and careful documentation alignment. The work enhanced maintainability by centralizing dtype handling logic, supporting robust model implementation and cross-model integration within the PaddleNLP framework.