> Zebra-Llama achieves Transformer-level accuracy with near-SSM efficiency using only 7–11B training tokens (compared to trillions of tokens required for pre-training) and an 8B teacher. Moreover, Zebra-Llama dramatically reduces KV cache size—down to 3.9%, 2%, and 2.73% of the original for the 1B, 3B, and 8B variants, respectively—while preserving 100%, 100%, and 97% of average zero-shot performance on LM Harness tasks.

This is an extraordinary claim, is there a catch I’m missing? Am I misreading?

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jychang 11 hours ago | parent [-]

The catch that you're missing is that Deepseek did this ages ago.

They're just using MLA, which is well known to reduce KV size by 90%. You know, the MLA that's used in... Deepseek V2, Deepseek V3, Deepseek R1, Deepseek V3.1, Deepseek V3.2.

Oh, and they also added some hybrid linear attention stuff to make it faster at long context. You know who else uses hybrid linear attention? Deepseek V3.2.

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storus 11 hours ago | parent | next [-]

Linear attention is really bad, it's only good for benchmaxing but it leads to a loss of valuable granularity, which can be felt in the latest DeepSeek randomly forgetting/ignoring/correcting explicitly stated facts in the prompt.

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erichocean 11 hours ago | parent | prev [-]

Kimi K2 also uses MLA, and Kimi Linear runs Kimi Delta Attention (it's SSM-like) for three out of every four layers (the fourth uses MLA).

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jychang 11 hours ago | parent [-]

Kimi K2 is literally a "copy Deepseek's homework" model. Seriously. It's even exactly 61 layers, the same as Deepseek V3/R1.

	▲	logicprog 8 hours ago \| parent [-]
		For a "copy Deepseek's homework" model, it's really good, preferable to DeepSeek for me (at least prior to V3.2, which I haven't been able to fully put through its paces yet). post-training really makes that much of a difference I guess