Ishrith Gowda developed SmoothQuant support for five new model architectures in the vllm-project/llm-compressor repository, expanding the model registry from nine to fourteen entries. Using Python and leveraging expertise in AI model optimization and machine learning, Ishrith updated the mappings registry to ensure consistency and maintainability across architectures such as Gemma2/3, Llama4, Mistral3, and Qwen3. The work involved validating that all new mappings loaded successfully and adhered to code quality standards, directly addressing a tracked issue. This enhancement improved inference efficiency for quantized models and reduced production risk by aligning registry structure with evolving architecture families.