Mixtral of Experts

Mixtral 8x7B — a new way to make smart language models

Meet Mixtral 8x7B, a model that spreads tasks across many tiny specialists so it can be both fast and clever.
It uses a Sparse Mixture of Experts setup: each layer have eight feedforward blocks and a small router picks two experts for every token, the pair can change every step.
That lets each token reach 47B parameters in total, while only using about 13B active parameters when running, so it saves compute.
It was trained to handle very long contexts, around 32k tokens, and it matches or beats much larger models on tests — especially in math, coding, and multilingual tasks.
There is an instruction-tuned Mixtral that outperforms several popular chat models on human evaluations, and both versions are released under Apache 2.
0 so people can try them.
The idea is simple: instead of one giant brain, many small experts share the work, and that can give smarter results with less constant cost.

Read article comprehensive review in Paperium.net:
Mixtral of Experts

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