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Two researchers claim to have developed Mamba, a new language modeling architecture that challenges the dominance of the Transformer, the widely used model since 2017.
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Mamba aims to match or surpass ChatGPT and other Transformer's language modeling capabilities while being faster and more cost-effective.
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Mamba introduces the Selective State Space Model (Selective SSM), inspired by state space models from the 1960s, to efficiently model language with linear and constant complexities for training and inference.
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Unlike Transformers, Mamba's selective approach allows it to compress context by choosing relevant information and discarding irrelevant data.
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Mamba's SSM module is input and time-dependent, making the model hardware-aware and capable of parallelization, addressing the limitations of earlier State Space Models.
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Despite promising results in tests with smaller sizes (up to 7 billion parameters), Mamba's performance at larger sizes is yet to be proven.
Mamba vs Other AI Models
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If successful at larger sizes, Mamba could potentially revolutionize language modeling, challenging the current dominance of models like ChatGPT based on the Transformer architecture.
The paper suggests that Mamba's efficient incorporation of state into a scalable solution makes it a noteworthy advancement in language modeling.