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Do we Need the Mamba Mindset when LLMs Fail? MoE Mamba and SSMs
Manage episode 447723509 series 3605861
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The research paper "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" explores a novel approach to language modeling by combining State Space Models (SSMs), which offer linear-time inference and strong performance in long-context tasks, with Mixture of Experts (MoE), a technique that scales model parameters while minimizing computational demands. The authors introduce MoE-Mamba, a model that interleaves Mamba, a recent SSM-based model, with MoE layers, resulting in significant performance gains and training efficiency. They demonstrate that MoE-Mamba outperforms both Mamba and standard Transformer-MoE architectures. The paper also explores different design choices for integrating MoE within Mamba, showcasing promising directions for future research in scaling language models beyond tens of billions of parameters.
Read it: https://arxiv.org/abs/2401.04081
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