Mark implemented dynamic AWQ mapping detection for hybrid attention models in the vllm-project/llm-compressor repository, focusing on improving quantization workflows and compatibility across architectures like Qwen3.5, Qwen3Next, and Llama-2B. Using Python and PyTorch, he replaced static, hardcoded mappings with logic that reads model configuration to identify layer types and distinguish between MoE and dense MLP structures. This approach enabled runtime layer-index selection and reduced manual maintenance. Mark also developed comprehensive unit tests to validate the new logic across representative configurations, demonstrating depth in deep learning model optimization and ensuring robust support for diverse model architectures.