Worked on expanding model support within the langgenius/dify repository by integrating Jamba and Llama 3.2 models, focusing on enhancing the system’s versatility for machine learning inference pipelines. The approach involved updating runtime configurations and implementing new model handling logic using Python and YAML, ensuring seamless deployment of the latest-generation models. This work addressed the need for broader model compatibility, reducing friction for customers adopting new AI technologies. Emphasis was placed on backend development and maintainability, with no critical bugs reported during the period. The updates contributed to faster time-to-value and improved competitive differentiation for users of the platform.