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.

翻译：许多在线提供的大规模预训练模型在下游任务迁移中表现出高效性。与此同时，基于这些预训练模型微调的各种任务特定模型也可在线供公众使用。在实践中，由于收集任务特定数据劳动密集，且微调大规模预训练模型计算开销巨大，复用任务特定微调模型来处理下游任务成为一种可行方案。然而，每个任务使用独立模型会带来沉重的存储和推理负担。近期，已提出多种免训练且参数节约的方法，用于将多个微调后的任务特定模型合并为一个多任务模型。然而，这些方法与每个任务单独使用微调模型相比，存在显著的精度差距。本文提出参数节约的微调模型复用方法（PERU）。针对全微调（FFT）模型复用，我们提出PERU-FFT方法，通过幅度剪枝将稀疏任务向量注入合并模型。针对LoRA微调模型复用，我们提出PERU-LoRA方法，利用低秩矩阵通过奇异值分解近似LoRA矩阵。PERU-FFT和PERU-LoRA均无需训练。在计算机视觉和自然语言处理任务上的大量实验表明，所提方法具有高效性和参数节约性。提出的PERU-FFT和PERU-LoRA以显著优势超越现有模型复用方法，且性能与每个任务单独使用微调模型相当。