Comment by hedgehog
13 hours ago
In initial testing on Ryzen 395 / Strix Halo it's about 22 tokens/s generation and the output quality is impressive. Better and faster than 3.8 27B, and enough better to justify moving away from 3.6 35B even though 35B is still faster. The Unsloth weights don't come with vision support but you can add that back yourself. llama.cpp recipe in case anyone else wants to save some time on setup: https://pastebin.com/fcqsbDTv
> Better... than 3.8 27B
How can a "6b active per token" MoE be better than a just released 27b in the same family? Possible maybe, but quite interesting and requiring some explanation.
Edit: ok, on second thought, probably because the small "experts" are really rich in specialized training compared to the dense model. Still raising questions about the details, e.g. the reasoning abilities (or all meta-skills) of a "6b per token" network compared to the dense capabilities...
Separating knowledge from reasoning so you only pay for what you use is a big rationale for MoE, the big problem being MoE training has historically been hard to get right. In a dense model every single token you're paying a cost to determine whether you're now talking about the flavor of durian.
Sure, the "6b subset" can be more knowledgeable on its area than a whole 27b generalist (and more efficient), but where is the simulated Intelligence encoded? A 6b subset as or more intelligent than a 27b raises the question of how metacognition skills are stored.
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We should still expect significant performance improvements.
I believe Unsloth’s branch (and ggml’s) don’t support MTP or the ngram embedding yet.
The active param count is so small I'm not sure how much advantage MTP will have, the current llama.cpp does load the ngram embeddings but I haven't verified it uses them. I expect to redeploy all this stuff every few days as the tooling gets improved.
My initial implementation of MTP I have here in my own (DGX Spark specific) custom runtime brought it up from ~12 tok/sec without MTP to ~16 to ~20 with; depending on workload.
It's not world changing, but at those speeds I'll take anything I can get.
(The ngram embeddings in this case are paged to/from disk which seems to cost ... basically nothing).
https://github.com/rdaum/eider/
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