Worked on the Furion-cn/sglang repository to address a critical reliability issue in model loading for Gemma2 LoRA weights. Focused on resolving a shape error by developing helper methods for input and output dimension inference within projection modules, and introduced a standardized mapping for module names to ensure consistent dimension retrieval across various weight configurations. This approach improved the stability and maintainability of the model loading process, reducing runtime errors and simplifying future extensions. Utilized Python and applied machine learning expertise, particularly in weight management and model loading, to enhance deployment efficiency and code quality for machine learning workflows.