A 7B hybrid attention-recurrent model outperforms its pure-transformer counterpart on pretraining metrics and scales more efficiently, supported by a proof that hybrids are strictly more expressive than either transformers or linear RNNs.
Ulysses distributes the sequence across devices and uses all-to-all communication to transpose from a sequence-parallel layout to a head-parallel layout before each layer
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Olmo Hybrid: From Theory to Practice and Back
A 7B hybrid attention-recurrent model outperforms its pure-transformer counterpart on pretraining metrics and scales more efficiently, supported by a proof that hybrids are strictly more expressive than either transformers or linear RNNs.