It means it's 8 7B models in a trench coat in a sense, it runs as fast as a 14B (2 experts at a time apparently) but takes up as much memory as a 40B model (70% * 8 * 7B). There is some process trained into it that chooses which experts to use based on the question posed. GPT 4 is allegedly based on the same architecture, but at 8*222B.
> GPT 4 is based on the same architecture, but at 8*222B.
Do we actually either no that it is MoE or that size? IIRC both if those started as outsidr guesses that somehow just became accepted knowledge without any actual confirmation.
In a MoE model with experts_per_token = 2 and each expert having 7B params, after picking the experts it should run as fast as the slowest 7B expert, not a comparable 14B model.
Does anyone here know roughly how an expert gets chosen? It seems like a very open-ended problem, and I'm not sure on how it can be implemented easily.
It's just a rough estimate given that these things are fairly linear, the original 7B mistral was 15 GB and the new one is 86 GB, whereas a fully duplicated 8 * 15 GB would suggest a 120 GB size, so 86/120 = 0.71 for actual size, suggesting 29% memory savings. This of course doesn't really account for any multiple vs single file saving overhead and such, so it's likely to be a bit off.
