Reinventing RNNs for the Transformer Era (RWKV)
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Combines parallelizable training of Transformers with efficient RNN inference.
Hybrid design: Achieves Transformer-style parallelizable training with RNN-style O(1) inference memory - best of both worlds.
Transformer-parity performance: Matches similarly-sized Transformers on language modeling benchmarks while being dramatically cheaper at inference.
Open community: Developed as an open-community project with releases spanning multiple scales and substantial community fine-tuning.
Post-Transformer contender: Alongside Mamba and RetNet, positioned as one of the credible attempts to dethrone attention for efficient long-context inference.
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