  Large pre-trained models have enabled significant advances in machine
learning and served as foundation components. Model fusion methods, such as
task arithmetic, have been proven to be powerful and scalable to incorporate
fine-tuned weights from different tasks into a multi-task model. However,
efficiently fine-tuning large pre-trained models on multiple downstream tasks
remains challenging, leading to inefficient multi-task model fusion. In this
work, we propose a novel method to improve multi-task fusion for
parameter-efficient fine-tuning techniques like LoRA fine-tuning. Specifically,
our approach partially linearizes only the adapter modules and applies task
arithmetic over the linearized adapters. This allows us to leverage the the
advantages of model fusion over linearized fine-tuning, while still performing
fine-tuning and inference efficiently. We demonstrate that our partial
linearization technique enables a more effective fusion of multiple tasks into
a single model, outperforming standard adapter tuning and task arithmetic
alone. Experimental results demonstrate the capabilities of our proposed
partial linearization technique to effectively construct unified multi-task
models via the fusion of fine-tuned task vectors. We evaluate performance over
an increasing number of tasks and find that our approach outperforms standard
parameter-efficient fine-tuning techniques. The results highlight the benefits
of partial linearization for scalable and efficient multi-task model fusion.
The code is available at https://github.com/tanganke/peta
