Understanding Model Merging with KnOTS
What is Model Merging?
Model merging is a technique that combines the strengths of different models to create a more versatile model capable of handling multiple tasks. This process allows for skill accumulation, fixing weaknesses, and improving existing models collaboratively.
Challenges with Current Methods
While model merging works well with full-rank fine-tuned (FFT) models, it faces challenges with parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA). Research shows that LoRA models have lower alignment in task updates, which complicates merging.
Innovative Solutions: KnOTS
Researchers from Georgia Tech and IBM have developed a new method called KnOTS (Knowledge Orientation Through SVD). This approach uses singular value decomposition (SVD) to transform task updates from different LoRA models into a common space. This makes it easier to merge models effectively.
Key Features of KnOTS
– **Versatile Compatibility**: KnOTS works well with existing merging techniques.
– **Joint Evaluation Benchmark**: It tests merged models on multiple datasets without specific context, providing a realistic measure of their performance.
– **Multi-Stage Architecture**: The method aligns and merges LoRA models through several stages, enhancing effectiveness.
Performance Improvements
Experimental results show that KnOTS significantly improves model performance:
– In vision tasks, merging eight ViT-B/32 models, KnOTS matches existing methods.
– With larger ViT-L/14 models, KnOTS-TIES outperforms baselines by up to 3%.
– In language tasks, KnOTS-TIES boosts accuracy by up to 2.9% compared to baseline methods.
Why Choose KnOTS?
KnOTS consistently enhances the performance of existing merging approaches by up to 4.3%, proving its robustness across various models and tasks. This method has the potential to create general, multi-task models by effectively aligning and merging LoRA representations.
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