Comment by nextos
8 days ago
The xLSTM could become a good alternative to transformers: https://arxiv.org/abs/2405.04517. On very long contexts, such as those arising in DNA models, these models perform really well.
There's a big state-space model comeback initiated by the S3-Mamba saga. RWKV, which is a hybrid between classical RNNs and transformers, is also worth mentioning.
I was just about to post this. There was a MLST podcast about it a few days ago:
https://www.youtube.com/watch?v=8u2pW2zZLCs
Lots of related papers referenced in the description.
One claim from that podcast was that the xLSTM attention mechanism is (in practical implementation) more efficient than (transformer) flash attention, and therefore promises to significantly reduces the time/cost of test-time compute.
Test it out here:
https://github.com/NX-AI/mlstm_kernels
https://huggingface.co/NX-AI/xLSTM-7b