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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.14956 |
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| _version_ | 1866913399913316352 |
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| author | Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren |
| author_facet | Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren |
| contents | Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unlocking the Global Synergies in Low-Rank Adapters Zhang, Zixi Zhang, Cheng Gao, Xitong Mullins, Robert D. Constantinides, George A. Zhao, Yiren Machine Learning Computation and Language Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted. |
| title | Unlocking the Global Synergies in Low-Rank Adapters |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2406.14956 |