The article studies how pretrained Transformers can be used to train state space models (SSMs), such as Mamba. The aim is to transfer knowledge accumulated during Transformer training to a different architecture.
Its summary highlights that SSMs may use less memory and achieve higher generation throughput than models based on attention mechanisms. These results motivate further study; they do not mean every implementation will achieve the same performance.
To evaluate the approach, define representative tasks and compare quality, memory use, and throughput under the same conditions. Record architectures, data, settings, and test limits so results can be interpreted and reproduced.
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