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Is that different from mixture of experts?
Yes. A mixture of experts is a single model that activates different routes though the same weights, with the route possibly changing literally on every token. It's not experts as in a bunch of standalone models that are good at specific high-level tasks.
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I edited the comment above
Not able to access the link, may be it is a private repo.
The models themselves are showing up on Hugging Face here: https://huggingface.co/prism-ml/models I've tried a couple in LM Studio - the GGUF one and the MLX one - but neither worked there. Anyone else get them to work? Might be that LM Studio needs to upgrade their llama.cpp or MLX engines first.
I can report that it's working in oMLX. I've been experimenting with the ternary one; it is quite an impressive model! I've been grilling it on some deep learning/computer vision stuff and it's aced everything so far. Responses are thorough, accurate, sophisticated. General knowledge outside of CS doesn't seem as robust, which I expected. Honestly, I don't think the examples in the blog post do it justice.
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I can report that it's working in oMLX. I've been experimenting with the ternary one; it is quite an impressive model! I've been grilling it on some deep learning/computer vision stuff and it's aced everything so far. Responses are thorough, accurate, sophisticated. General knowledge outside of CS doesn't seem as robust, which I expected. Honestly, I don't think the examples in the blog post do it justice.
Nice, I tried it too with oMLX — agreed, it seems very capable for coding! Was slightly underwhelmed by performance though. I got about ~24 t/s on the ternary version on my M2 Max 64. That’s quite a bit slower than Qwen A3B 35B (4 bit unsloth). How was perf for you?
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Nice, I tried it too with oMLX — agreed, it seems very capable for coding! Was slightly underwhelmed by performance though. I got about ~24 t/s on the ternary version on my M2 Max 64. That’s quite a bit slower than Qwen A3B 35B (4 bit unsloth). How was perf for you?
I had roughly the same performance on M3 Max 64: ~30 tokens/sec. Which is not terrible, but with the latest Lightning MTP optimization I am getting ~100 tokens/sec with Qwen 3.6 35B-A3B.
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I had roughly the same performance on M3 Max 64: ~30 tokens/sec. Which is not terrible, but with the latest Lightning MTP optimization I am getting ~100 tokens/sec with Qwen 3.6 35B-A3B.
WHAT. that‘s amazing. thanks so much for sharing, i‘ve been looking for ways to speed a3b up for days. It‘s 11pm here but i‘ll try this right now
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I think this is where leveraging classifier models will become important. The frontier LLM models do "everything", while we've known for a while that to truly scale this we will need to distill models into their individual functions. I don't see this as necessarily a bad thing and hope more is done in this space. Very promising.
Bitter lesson is knocking. Mixture of experts is essentially what you’re describing but free from unnecessary inductive biases.
2) Bitter lesson is misunderstood. Specialization and inductive biases still matter. ChatGPT isn’t the best chess player in the world just because it’s seen more math problems or read more Japanese poetry. Stockfish is, because it bakes in useful inductive biases like minimax.