Update README.md
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@article
{lu2024reassessing,
title={Reassessing Layer Pruning in LLMs: New Insights and Methods},
author={Lu, Yao and Cheng, Hao and Fang, Yujie and Wang, Zeyu and Wei, Jiaheng and Xu, Dongwei and Xuan, Qi and Yang, Xiaoniu and Zhu, Zhaowei},
journal={arXiv preprint arXiv:2411.15558},
year={2024}
}
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README.md
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@@ -42,3 +42,13 @@ Zero-shot performance. Evaluated using select datasets from the [LM Evaluation H
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| PIQA | HellaSwag | OpenbookQA | ARC-e | ARC-c | MMLU | CMMLU | WinoGrande |
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| 0.7383±0.0103 | 0.5323±0.0050 | 0.3080±0.0207 | 0.7260±0.0092 | 0.4684±0.0146 | 0.6567±0.0038 | 0.5515±0.0045 | 0.6646±0.0133 |
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| PIQA | HellaSwag | OpenbookQA | ARC-e | ARC-c | MMLU | CMMLU | WinoGrande |
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| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
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| 0.7383±0.0103 | 0.5323±0.0050 | 0.3080±0.0207 | 0.7260±0.0092 | 0.4684±0.0146 | 0.6567±0.0038 | 0.5515±0.0045 | 0.6646±0.0133 |
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~~~
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@article{lu2024reassessing,
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title={Reassessing Layer Pruning in LLMs: New Insights and Methods},
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author={Lu, Yao and Cheng, Hao and Fang, Yujie and Wang, Zeyu and Wei, Jiaheng and Xu, Dongwei and Xuan, Qi and Yang, Xiaoniu and Zhu, Zhaowei},
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journal={arXiv preprint arXiv:2411.15558},
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year={2024}
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}
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~~~
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