Worked on the jeejeelee/vllm repository to enhance the resilience of model loading workflows by addressing a critical crash scenario. Focused on backend development using Python, the work involved implementing defensive programming techniques to auto-populate missing architectures when hf_config.architectures was None, based on the detected model type. This fix prevented TypeErrors during model loading, reducing deployment downtime and improving production readiness. The solution strengthened configuration handling and data modeling within the inference tooling, resulting in more reliable deployments and smoother user experiences. Emphasis was placed on robust unit testing to ensure stability and minimize support overhead in production environments.