Zebra-Llama – Towards efficient hybrid models

If the claims in the abstract are true, then this is legitimately revolutionary. I don’t believe it. There are probably some major constraints/caveats that keep these results from generalizing. I’ll read through the paper carefully this time instead of a skim and come back with thoughts after I’ve digested it.

What's not to believe? Qwerky-32b has already done something similar as a finetune of QwQ-32b but not using traditional attention architecture.

And hybrid models aren't new, MLA based hybrid models is basically just Deepseek V3.2 in a nutshell. Note that Deepseek V3.2 (and V3.1, R1, and V3... and V2 actually) all use MLA. Deepseek V3.2 is what adds the linear attention stuff.

Actually, since Deepseek V3.1 and Deepseek V3.2 are just post-training on top of the original Deepseek V3 pretrain run, I'd say this paper is basically doing exactly what Deepseek V3.2 did in terms of efficiency.

DeepSeek-V3.2 is a sparse attention architecture, while Zebra-Llama is a hybrid attention/SSM architecture. The outcome might be similar in some ways (close to linear complexity) but I think they are otherwise quite different.