During February 2026, Jeejee Lee contributed to the jeejeelee/vllm repository by implementing NVFP4 quantization support for the Step3.5-Flash model. This work focused on enabling memory-efficient and faster inference for large-scale machine learning workloads. Jeejee Lee used Python and PyTorch to introduce new test cases and refactor core classes, ensuring compatibility with various activation functions throughout the quantization pipeline. The changes improved memory management and inference performance, allowing for more cost-effective scaling of large models. The depth of the work demonstrated strong proficiency in quantization techniques, test automation, and collaborative development within a complex codebase.