LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training

• URL: http://arxiv.org/abs/2406.16554v1
• Date: Mon, 24 Jun 2024 11:43:07 GMT
• Title: LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
• Authors: Tong Zhu, Xiaoye Qu, Daize Dong, Jiacheng Ruan, Jingqi Tong, Conghui He, Yu Cheng,
• Abstract summary: 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. Our LLaMA-MoE models significantly outperform dense models that contain similar activation parameters.
• Score: 21.359073227913303
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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. The source codes and models are available at https://github.com/pjlab-sys4nlp/llama-moe .

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