Over two months, Oneraghavan contributed to ROCm/vllm and jeejeelee/vllm, focusing on reliability and maintainability in machine learning infrastructure. In ROCm/vllm, Oneraghavan clarified CLI parameter documentation for Matryoshka Embeddings and implemented prompt token truncation in AsyncLLM, improving input size management and user guidance. For jeejeelee/vllm, Oneraghavan addressed a critical bug in Qwen3-MoE checkpoint loading by introducing a remapping function for FP8 k_scale and v_scale parameters, enhancing compatibility across checkpoint formats. The work demonstrated depth in Python, asynchronous programming, and model optimization, with careful attention to documentation and robust, production-ready code improvements.