Worked on internal correctness stabilization for w4a16-mxfp4 quantization within the vllm-ascend repository, focusing on improving inference stability and reducing regression risk in quantized machine learning models. Addressed a critical bug by adapting quantization techniques and updating unit tests to align with the new quantization path, ensuring reliability and maintainability. The work involved validating changes against the vLLM 0.23.x baseline using Qwen3-Coder-30B-A3B-Instruct_MXFP4 weights, with no user-facing modifications to APIs or configuration. Demonstrated proficiency in Python, PyTorch, and test-driven development, while collaborating across repositories to maintain compatibility with the main branch.