Worked on stabilizing and optimizing the CPU path for Mixture of Experts (MoE) within the jeejeelee/vllm repository, focusing on production readiness and maintainability. Addressed a critical initialization issue in the CPU fused MoE activation by replacing the SiluAndMul instantiation with the native SiLU forward method, which improved both robustness and performance for CPU workloads. Emphasized code hygiene and traceability by providing clear commit documentation suitable for production audits. Utilized Python and PyTorch to refine machine learning infrastructure, resulting in a more predictable and stable activation path that supports reliable deployment and simplifies future maintenance for CPU-based MoE models.