Sakib contributed to two repositories over a two-month period, focusing on both documentation and deep learning model optimization. For cocktailpeanut/HunyuanVideoGP, Sakib enhanced documentation quality by introducing a Chinese README and integrating Replicate platform visibility, improving onboarding and external integration readiness. In replicate/cog-flux, Sakib engineered hardware-aware FP8 optimization for the KREA model using Python, with automatic hardware detection and a BF16 fallback for A100 GPUs to ensure stable production inference. These updates reduced latency and memory footprint, streamlining production deployment across GPU fleets. Sakib’s work demonstrated depth in configuration management, model optimization, and cross-platform documentation practices.