The Mamba architecture presents a novel approach to autoregressive language modeling, achieving competitive performance with attention-free design, further optimizing speed and memory.
Our extensive empirical evaluations reveal that the selective state space models show promising results in various synthetic tasks and real-world applications including language modeling.
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