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Mamba Explained

thegradient.pub · 4,842 words · saved by 1 readers

And by AI, I mean Transformers. Practically all the big breakthroughs in AI over the last few years are due to Transformers. Mamba, however, is one of an alternative class of models called State Space Models (SSMs). Importantly, for the first time, Mamba promises similar performance (and crucially similar scaling laws) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens). To achieve this long context, the Mamba authors remove the “quadratic bottleneck” in the Attention Mechanism. Mamba also runs fast - like “up to 5x faster than Transformer fast”1. Gu and Dao, the Mamba authors write: Mamba enjoys fast inference and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modelling, our Mamba-3B model outperforms Transformers of the same size

The State Space Model taking on Transformers Right now, AI is eating the world. And by AI, I mean Transformers. Practically all the big breakthroughs in AI over the last few years are due to Transformers. Mamba , however, is one of an alternative class of models called State Space Models ( SSMs ). Importantly, for the first time, Mamba promises similar performance (and crucially similar scaling laws ) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens). To achieve this long context, the Mamba authors remove the “quadratic bottleneck” in the Attention Mechan

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