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Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning
Sanghyeon Kim1, Hyunmo Yang2, Yunghyun Kim2
1Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066, Seoubu-ro, Suwon, 16419, Republic of Korea.
Summary
Hydra enhances foundation model adaptation by combining parallel and sequential adapter branches. This novel approach improves parameter efficiency and generalization for diverse downstream tasks, outperforming existing methods.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Large-scale foundation models require efficient adaptation techniques for downstream tasks.
- Low-rank adaptation (LoRA) methods offer parameter efficiency and no inference latency.
Purpose of the Study:
- To introduce Hydra, a more general adapter module for foundation models.
- To integrate parallel and sequential adaptation branches for enhanced expressiveness and fine-tuning exploration.
Main Methods:
- Hydra combines parallel and sequential adaptation branches.
- It leverages pre-trained weights via linear combination of features.
- Comprehensive analysis of adaptation branch characteristics was performed.
Main Results:
- Hydra demonstrates superior performance and efficiency in extensive experiments.
- The integrated approach allows exploration of broader optimal fine-tuning points.
- Learned features show improved generalization across diverse downstream tasks.
Conclusions:
- Hydra offers a more expressive and effective method for adapting foundation models.
- The combined branch strategy enhances generalization and fine-tuning capabilities.
- This approach has significant potential for various AI applications.
