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

2 citations · 3 across the 3 of their papers we have counts for

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cs.CL2024

LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training

Xiaoye Qu, Daize Dong, Xuyang Hu +3

Recently, inspired by the concept of sparsity, Mixture-of-Experts (MoE) models have gained increasing popularity for scaling model size while keeping the number of activated parame…

cs.CL2024★ 2 cited

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

Tong Zhu, Xiaoye Qu, Daize Dong +4

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…

cs.CL2024

Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts

Tong Zhu, Daize Dong, Xiaoye Qu +3

Mixture-of-Experts (MoE) models have shown remarkable capability in instruction tuning, especially when the number of tasks scales. However, previous methods simply merge all train…