During February 2026, this developer contributed to the meta-pytorch/forge repository by delivering multi-accelerator support through a hardware-agnostic refactor of the SFT component. The work focused on enabling seamless deployment across CPUs, GPUs, and other accelerators, reducing environment-specific maintenance and simplifying the onboarding of new hardware. Using Python and leveraging PyTorch, the developer implemented a unified code path that enhances portability and flexibility for machine learning workflows. This foundational change set the stage for future accelerator integrations and standardized testing, and was completed in collaboration with both internal and external contributors, reflecting a collaborative and technically rigorous approach.