Worked on standardizing weight loading for the MistralLarge3ForCausalLM model within the jeejeelee/vllm repository, focusing on improving maintainability and consistency across model implementations. The approach involved migrating from a regex-based remapping process to a structured pipeline using AutoWeightsLoader and WeightsMapper, which streamlined the loading of model weights and reduced the potential for errors in production environments. This transition not only simplified onboarding for new models but also lowered ongoing maintenance overhead. The work leveraged skills in machine learning model architecture, PyTorch, and Python, emphasizing stabilization and long-term reliability rather than introducing new features or bug fixes.