MoTE: Mixture of Ternary Experts for Memory-efficient Large Multimodal
  Models
Authors: name: Hongyu Wang, name: Jiayu Xu, name: Ruiping Wang, name: Yan Feng, name: Yitao Zhai, name: Peng Pei, name: Xunliang Cai, name: Xilin Chen

Large multimodal Mixture-of-Experts (MoEs) effectively scale the model size
to boost performance while maintaining fixed active parameters. However,
previous works primarily utilized full-precision experts during sparse
up-cycling. Despite they show superior performance on end tasks, the large
amount of experts introduces higher memory footprint, which poses significant
challenges for the deployment on edge devices. In this work, we propose MoTE, a
scalable and memory-efficient approach to train Mixture-of-Ternary-Experts
models from dense checkpoint. Instead of training fewer high-precision experts,
we propose to train more low-precision experts during up-cycling. Specifically,
we use the pre-trained FFN as a shared expert and train ternary routed experts
with parameters in {-1, 0, 1}. Extensive experiments show that our approach has
promising scaling trend along model size. MoTE achieves comparable performance
to full-precision baseline MoE-LLaVA while offering lower memory footprint.
Furthermore, our approach is compatible with post-training quantization methods
and the advantage further amplifies when memory-constraint goes lower. Given
the same amount of expert memory footprint of 3.4GB and combined with
post-training quantization, MoTE outperforms MoE-LLaVA by a gain of 4.3%
average accuracy on end tasks, demonstrating its effectiveness and potential
for memory-constrained devices.