State-space models (SSMs) have recently demonstrated competitive performance
to transformers at large-scale language modeling benchmarks while achieving
linear time and memory complexity as a function of sequence length. Mamba, a
recently released SSM model, shows impressive performance in both language
modeling and long sequence processing tasks. Simultaneously, mixture-of-expert
(MoE) models have shown remarkable performance while significantly reducing the
compute and latency costs of inference at the expense of a larger memory
footprint. In this paper, we present BlackMamba, a novel architecture that
combines the Mamba SSM with MoE to obtain the benefits of both. We demonstrate
that BlackMamba performs competitively against both Mamba and transformer
baselines, and outperforms in inference and training FLOPs. We fully train and
open-source 340M/1.5B and 630M/2.8B BlackMamba models on 300B tokens of a
custom dataset. We show that BlackMamba inherits and combines both of the
benefits of SSM and MoE architectures, combining linear-complexity generation
from SSM with cheap and fast inference from MoE. We release all weights,
checkpoints, and inference code open-source. Inference code at:
this https URL

利用 Mamba SSM 和 MoE 相结合的新型架构 BlackMamba，在模型训练和推理 FLOPs 方面表现优秀，实现了 SSM 的线性复杂度生成和 MoE 快速高效推理的结合。