HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918102798696448 |
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| author | Ceritli, Taha Bohdal, Ondrej Ozay, Mete Moon, Jijoong Lee, Kyeng-Hun Ko, Hyeonmok Michieli, Umberto |
| author_facet | Ceritli, Taha Bohdal, Ondrej Ozay, Mete Moon, Jijoong Lee, Kyeng-Hun Ko, Hyeonmok Michieli, Umberto |
| contents | Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environments such as mobile devices. Although model merging techniques can reduce storage costs, they typically result in substantial performance degradation. In this work, we introduce HydraOpt, a new model merging technique that capitalizes on the inherent similarities between the matrices of low-rank adapters. Unlike existing methods that produce a fixed trade-off between storage size and performance, HydraOpt allows us to navigate this spectrum of efficiency and performance. Our experiments show that HydraOpt significantly reduces storage size (48% reduction) compared to storing all adapters, while achieving competitive performance (0.2-1.8% drop). Furthermore, it outperforms existing merging techniques in terms of performance at the same or slightly worse storage efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging Ceritli, Taha Bohdal, Ondrej Ozay, Mete Moon, Jijoong Lee, Kyeng-Hun Ko, Hyeonmok Michieli, Umberto Machine Learning Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environments such as mobile devices. Although model merging techniques can reduce storage costs, they typically result in substantial performance degradation. In this work, we introduce HydraOpt, a new model merging technique that capitalizes on the inherent similarities between the matrices of low-rank adapters. Unlike existing methods that produce a fixed trade-off between storage size and performance, HydraOpt allows us to navigate this spectrum of efficiency and performance. Our experiments show that HydraOpt significantly reduces storage size (48% reduction) compared to storing all adapters, while achieving competitive performance (0.2-1.8% drop). Furthermore, it outperforms existing merging techniques in terms of performance at the same or slightly worse storage efficiency. |
| title | HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.17706 |