Akintunde Oladipo developed Eagle3 speculative decoding support for the Mistral3ForConditionalGeneration model in the jeejeelee/vllm repository, focusing on enhancing multi-modal input processing. Using Python and leveraging deep learning and model optimization techniques, Akintunde implemented a solution that reduces inference latency and improves generation quality for multi-modal prompts. The work involved close collaboration with contributors through code reviews and co-authored commits, ensuring robust integration and readiness for downstream applications. By addressing Eagle3 support, Akintunde laid the foundation for future performance benchmarking and additional multi-modal features, demonstrating depth in both technical execution and collaborative software engineering practices.