Scalable Model Merging with Progressive Layer-wise Distillation
This addresses the challenge of efficiently integrating multiple fine-tuned models for practitioners in machine learning, with incremental improvements over existing few-shot methods.
The paper tackles the problem of performance degradation in model merging when little or no data is available, by introducing ProDistill, a few-shot merging algorithm that achieves state-of-the-art performance with improvements of up to 6.14% and 6.61% in vision and NLU tasks.
Model merging offers an effective way to integrate the capabilities of multiple fine-tuned models. However, the performance degradation of the merged model remains a challenge, particularly when none or few data are available. This paper first highlights the necessity of domain-specific data for model merging by proving that data-agnostic algorithms can have arbitrarily bad worst-case performance. Building on this theoretical insight, we explore the relationship between model merging and distillation, introducing a novel few-shot merging algorithm, ProDistill (Progressive Layer-wise Distillation). Unlike common belief that layer wise training hurts performance, we show that layer-wise teacher-student distillation not only enhances the scalability but also improves model merging performance. We conduct extensive experiments to show that compared to existing few-shot merging methods, ProDistill achieves state-of-the-art performance, with up to 6.14% and 6.61% improvements in vision and NLU tasks. Furthermore, we extend the experiments to models with over 10B parameters, showcasing the exceptional scalability of ProDistill.