Many pre-trained large-scale models provided online have become highly
effective in transferring to downstream tasks. At the same time, various
task-specific models fine-tuned on these pre-trained models are available
online for public use. In practice, as collecting task-specific data is
labor-intensive and fine-tuning the large pre-trained models is computationally
expensive, one can reuse task-specific finetuned models to deal with downstream
tasks. However, using a model per task causes a heavy burden on storage and
serving. Recently, many training-free and parameter-efficient methods have been
proposed for reusing multiple fine-tuned task-specific models into a single
multi-task model. However, these methods exhibit a large accuracy gap compared
with using a fine-tuned model per task. In this paper, we propose
Parameter-Efficient methods for ReUsing (PERU) fine-tuned models. For reusing
Fully Fine-Tuned (FFT) models, we propose PERU-FFT by injecting a sparse task
vector into a merged model by magnitude pruning. For reusing LoRA fine-tuned
models, we propose PERU-LoRA use a lower-rank matrix to approximate the LoRA
matrix by singular value decomposition. Both PERUFFT and PERU-LoRA are
training-free. Extensive experiments conducted on computer vision and natural
language process tasks demonstrate the effectiveness and parameter-efficiency
of the proposed methods. The proposed PERU-FFT and PERU-LoRA outperform
existing reusing model methods by a large margin and achieve comparable
performance to using a fine-tuned model per task.

提出了针对使用多个经过精调的特定任务模型的参数高效重用方法 PERU。通过稀疏任务向量注入到合并模型中，通过截断幅值来创造 PERU-FFT，以重用完全精调的模型。通过奇异值分解来近似 LoRA 矩阵，提出了使用低秩矩阵重用 LoRA 精调模型的 PERU-LoRA。在计算机视觉和自然语言处理任务上进行了大量实验证明了该方法的有效性和参数效率。与现有的重用模型方法相比，提出的 PERU-FFT 和 PERU-LoRA 取得了明显优势，并且在性能上与每个任务使用精调模型相当。