  Tangent Model Composition (TMC) is a method to combine component models
independently fine-tuned around a pre-trained point. Component models are
tangent vectors to the pre-trained model that can be added, scaled, or
subtracted to support incremental learning, ensembling, or unlearning.
Component models are composed at inference time via scalar combination,
reducing the cost of ensembling to that of a single model. TMC improves
accuracy by 4.2% compared to ensembling non-linearly fine-tuned models at a
2.5x to 10x reduction of inference cost, growing linearly with the number of
component models. Each component model can be forgotten at zero cost, with no
residual effect on the resulting inference. When used for continual
fine-tuning, TMC is not constrained by sequential bias and can be executed in
parallel on federated data. TMC outperforms recently published continual
fine-tuning methods almost uniformly on each setting -- task-incremental,
class-incremental, and data-incremental -- on a total of 13 experiments across
3 benchmark datasets, despite not using any replay buffer. TMC is designed for
composing models that are local to a pre-trained embedding, but could be
extended to more general settings. The code is available at:
https://github.com/tianyu139/tangent-model-composition
