Mamba, a new State Space Model (SSM), presents an alternative to the dominant Transformer architecture in AI. It aims to match Transformer performance and scaling laws while efficiently handling extremely long sequences, potentially up to one million tokens. This is achieved by removing the quadratic bottleneck found in Transformer attention mechanisms, allowing for faster inference and linear scaling with sequence length. Mamba has demonstrated state-of-the-art results across various modalities, including language, audio, and genomics, outperforming Transformers of similar or even larger sizes. AI
排序理由 This is a research paper describing a new model architecture, Mamba, which is presented as an alternative to Transformers.
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