AI This Week: Unveiling Mamba-2, The Game-Changer in State Space Model Architecture
After the success of Mamba-1, researchers Tri Dao and Albert Gu have introduced Mamba-2, a new state space model architecture that outperforms Mamba and Transformer++. Mamba-2 shows promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of transformers.
The core innovation of Mamba-2, Structured State Space Duality (SSD), combines SSMs and attention mechanisms. SSD constrains the recurrent matrix ?? to a scalar-times-identity structure, simplifying computations and enhancing hardware efficiency. This design enables multi-head SSMs, increasing the state size from 16 in Mamba-1 to 64-256 in Mamba-2, and uses matrix multiplications optimized for GPUs/TPUs.
Mamba-2 trains 50% faster than Mamba-1 and handles larger state dimensions. At the 3B scale, Mamba-2, trained on 300B tokens, surpasses Mamba-1 and older transformers in performance. The model significantly outperforms Mamba-1 on tasks like multi-query associative recall (MQAR) due to its larger state sizes.
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