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The AI community is abuzz with the introduction of Mamba, an innovative selective state space model (SSM) that addresses limitations of both Transformers and traditional SSMs. Mamba's architecture integrates selective SSMs into a neural network, removing attention and MLP blocks. The model aims to improve sequence modeling by overcoming drawbacks associated with transformers. Additionally, Mamba-2, an advanced version of Mamba, has been released. Mamba-2 features 8x larger states, 50% faster training, and outperforms both Mamba and Transformer++ in perplexity and wall-clock time. This development is part of a broader theoretical framework called state space duality, which shows connections between SSMs and linear attention. The advancements are significant for fields like LLMs, DeepLearning, and DataScience.
excited to finally release Mamba-2!! 8x larger states, 50% faster training, and even more S's 🐍🐍 Mamba-2 aims to advance the theory of sequence models, developing a framework of connections between SSMs and (linear) attention that we call state space duality (SSD) w/@tri_dao https://t.co/xbNMzMeYL8
With @_albertgu, we’ve built a rich theoretical framework of state-space duality, showing that many linear attn variants and SSMs are equivalent! The resulting model, Mamba-2 is better & faster than Mamba-1, and still matching strong Transformer arch on language modeling. 1/ https://t.co/mqDwiYeSAl
[LG] Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality T Dao, A Gu [Princeton University & CMU] (2024) https://t.co/vkSNhzVgeq - This paper shows theoretical connections between structured state space models (SSMs),… https://t.co/1BwiroVH3l
