A Note on LoRA
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913305105268736 |
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| author | Fomenko, Vlad Yu, Han Lee, Jongho Hsieh, Stanley Chen, Weizhu |
| author_facet | Fomenko, Vlad Yu, Han Lee, Jongho Hsieh, Stanley Chen, Weizhu |
| contents | LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the original LoRA paper by offering new perspectives that were not initially discussed and presents a series of insights for deploying LoRA at scale. Without introducing new experiments, we aim to improve the understanding and application of LoRA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05086 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Note on LoRA Fomenko, Vlad Yu, Han Lee, Jongho Hsieh, Stanley Chen, Weizhu Machine Learning Artificial Intelligence Computation and Language LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the original LoRA paper by offering new perspectives that were not initially discussed and presents a series of insights for deploying LoRA at scale. Without introducing new experiments, we aim to improve the understanding and application of LoRA. |
| title | A Note on LoRA |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2404.05086 |