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Dec 20, 2021 · This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range ...
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- ...
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: ...
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This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- ...
Dec 21, 2021 · Demonstrate performance of the large-scale language models with mixture-of-experiments by experiments. The dense model architecture is based on ...
Feb 8, 2022 · Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed ...
The study suggests that Switch Transformer and Transformer models are the most suitable for the given task, given their high performance and faster training and ...
Abstract. Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed ...
Mixture of expert (MoE) models are efficient because they leverage sparse computation, i.e., only a small fraction of parameters are active for any given input.
Apr 23, 2024 · The primary benefit of employing MoE in language models is the ability to scale up the model size while maintaining a relatively constant ...