@inproceedings{zhu-etal-2024-llama,
title = "{LL}a{MA}-{M}o{E}: Building Mixture-of-Experts from {LL}a{MA} with Continual Pre-Training",
author = "Zhu, Tong and
Qu, Xiaoye and
Dong, Daize and
Ruan, Jiacheng and
Tong, Jingqi and
He, Conghui and
Cheng, Yu",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.890/",
doi = "10.18653/v1/2024.emnlp-main.890",
pages = "15913--15923",
abstract = "Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale setting still suffers from data-hungry and instability problems. Motivated by this limit, we investigate building MoE models from existing dense large language models. Specifically, based on the well-known LLaMA-2 7B model, we obtain an MoE model by: (1) Expert Construction, which partitions the parameters of original Feed-Forward Networks (FFNs) into multiple experts; (2) Continual pre-training, which further trains the transformed MoE model and additional gate networks. In this paper, we comprehensively explore different methods for expert construction and various data sampling strategies for continual pre-training. After these stages, our LLaMA-MoE models could maintain language abilities and route the input tokens to specific experts with part of the parameters activated. Empirically, by training 200B tokens, LLaMA-MoE-3.5B models significantly outperform dense models that contain similar activation parameters."
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<abstract>Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale setting still suffers from data-hungry and instability problems. Motivated by this limit, we investigate building MoE models from existing dense large language models. Specifically, based on the well-known LLaMA-2 7B model, we obtain an MoE model by: (1) Expert Construction, which partitions the parameters of original Feed-Forward Networks (FFNs) into multiple experts; (2) Continual pre-training, which further trains the transformed MoE model and additional gate networks. In this paper, we comprehensively explore different methods for expert construction and various data sampling strategies for continual pre-training. After these stages, our LLaMA-MoE models could maintain language abilities and route the input tokens to specific experts with part of the parameters activated. Empirically, by training 200B tokens, LLaMA-MoE-3.5B models significantly outperform dense models that contain similar activation parameters.</abstract>
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%0 Conference Proceedings
%T LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training
%A Zhu, Tong
%A Qu, Xiaoye
%A Dong, Daize
%A Ruan, Jiacheng
%A Tong, Jingqi
%A He, Conghui
%A Cheng, Yu
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhu-etal-2024-llama
%X Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale setting still suffers from data-hungry and instability problems. Motivated by this limit, we investigate building MoE models from existing dense large language models. Specifically, based on the well-known LLaMA-2 7B model, we obtain an MoE model by: (1) Expert Construction, which partitions the parameters of original Feed-Forward Networks (FFNs) into multiple experts; (2) Continual pre-training, which further trains the transformed MoE model and additional gate networks. In this paper, we comprehensively explore different methods for expert construction and various data sampling strategies for continual pre-training. After these stages, our LLaMA-MoE models could maintain language abilities and route the input tokens to specific experts with part of the parameters activated. Empirically, by training 200B tokens, LLaMA-MoE-3.5B models significantly outperform dense models that contain similar activation parameters.
%R 10.18653/v1/2024.emnlp-main.890
%U https://aclanthology.org/2024.emnlp-main.890/
%U https://doi.org/10.18653/v1/2024.emnlp-main.890
%P 15913-15923
Markdown (Informal)
[LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training](https://aclanthology.org/2024.emnlp-main.890/) (Zhu et al., EMNLP 2024)
ACL
- Tong Zhu, Xiaoye Qu, Daize Dong, Jiacheng Ruan, Jingqi Tong, Conghui He, and Yu Cheng. 2024. LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15913–15923, Miami, Florida, USA. Association for Computational Linguistics.