Gupta Algebra enhanced the video inference capabilities of the huggingface/transformers repository by adding half-precision (fp16) support to the SAM2 model. This work focused on improving performance and memory efficiency by implementing robust dtype handling and casting memory attention inputs to match the inference session’s data type, reducing precision-related errors. Gupta expanded unit test coverage to validate both fp16 and fp32 behaviors across diverse video inputs, ensuring reliability and regression resistance. Collaborating with Yonigozlan, Gupta also improved code maintainability through formatting updates. The project leveraged Python and deep learning techniques, demonstrating thoughtful engineering depth in video processing and machine learning workflows.