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article · 2024

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Damai Dai, Chengqi Deng, Chenggang Zhao, Ruijian Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Yongle Wu, Zhenda Xie, Y. K. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang

Cited by 152Open access

Abstract

Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Cite this paper

APA 7

Dai, D., Deng, C., Zhao, C., Xu, R., Gao, H., Chen, D., Li, J., Zeng, W., Yu, X., Wu, Y., Xie, Z., Li, Y. K., Huang, P., Luo, F., Ruan, C., Sui, Z., & Liang, W. (2024). DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models. https://doi.org/10.18653/v1/2024.acl-long.70

MLA 9

Dai, Damai, et al. “DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.” 2024, pp. 1280–97, https://doi.org/10.18653/v1/2024.acl-long.70.

Chicago

Dai, Damai, Chengqi Deng, Chenggang Zhao, Ruijian Xu, Huazuo Gao, Deli Chen, Jiashi Li, et al. 2024. “DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.” https://doi.org/10.18653/v1/2024.acl-long.70.

Harvard

Dai, D. et al. (2024) ‘DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models’. Available at: https://doi.org/10.18653/v1/2024.acl-long.70.

BibTeX
@article{dai2024deepseekmoe,
  title = {{DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models}},
  author = {Dai, Damai and Deng, Chengqi and Zhao, Chenggang and Xu, Ruijian and Gao, Huazuo and Chen, Deli and Li, Jiashi and Zeng, Wangding and Yu, Xingkai and Wu, Yongle and Xie, Zhenda and Li, Y. K. and Huang, Panpan and Luo, Fuli and Ruan, Chong and Sui, Zhifang and Liang, Wenfeng},
  pages = {1280--1297},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.70}
}

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