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The paper proposes combining State Space Models (SSMs) with Mixture of Experts (MoE) to unlock the scaling potential of SSMs. The resulting model, MoE-Mamba, outperforms both Mamba and Transformer-MoE in terms of performance and training steps.
https://arxiv.org/abs//2401.04081
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
The paper proposes combining State Space Models (SSMs) with Mixture of Experts (MoE) to unlock the scaling potential of SSMs. The resulting model, MoE-Mamba, outperforms both Mamba and Transformer-MoE in terms of performance and training steps.
https://arxiv.org/abs//2401.04081
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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