3 citations · 12 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters
Shwai He, Liang Ding, Daize Dong +2
Adapter Tuning, which freezes the pretrained language models (PLMs) and only fine-tunes a few extra modules, becomes an appealing efficient alternative to the full model fine-tunin…