  Generalist models, which are capable of performing diverse multi-modal tasks
in a task-agnostic way within a single model, have been explored recently.
Being, hopefully, an alternative to approaching general-purpose AI, existing
generalist models are still at an early stage, where modality and task coverage
is limited. To empower multi-modal task-scaling and speed up this line of
research, we release a generalist model learning system, OFASys, built on top
of a declarative task interface named multi-modal instruction. At the core of
OFASys is the idea of decoupling multi-modal task representations from the
underlying model implementations. In OFASys, a task involving multiple
modalities can be defined declaratively even with just a single line of code.
The system automatically generates task plans from such instructions for
training and inference. It also facilitates multi-task training for diverse
multi-modal workloads. As a starting point, we provide presets of 7 different
modalities and 23 highly-diverse example tasks in OFASys, with which we also
develop a first-in-kind, single model, OFA+, that can handle text, image,
speech, video, and motion data. The single OFA+ model achieves 95% performance
in average with only 16% parameters of 15 task-finetuned models, showcasing the
performance reliability of multi-modal task-scaling provided by OFASys.
Available at https://github.com/OFA-Sys/OFASys
