This paper details the model that's been in the wild for approximately a month now. Mixtral 8x7B is very, very good. It's roughly sized at 13B, and ranked much, much higher than competitively sized models by, e.g.
https://www.reddit.com/r/LocalLLaMA/comments/1916896/llm_com.... Ravenwolf notes that the model slightly outperforms some of its benchmark testing, and this is my experience. It's surprisingly good for a model of its size, and a very capable daily driver on a Mac for chat, code input and other uses.
Something that has come to light since the release of the weights, and not mentioned in this paper is that it looks like fairly likely that the 8 experts were all seeded by Mistral 7B and subsequently diverged. This has generated a lot of experimentation in the local LLM community with cloning models as a way to cheaply generate experts.
It was generally thought likely that training an 8x7B network would be as much work as training 8 7B networks, but this seems not to have been true for Mistral, which is super interesting.
There's still a lot of rapid innovation happening in this space, with papers like Calm from DeepMind this week, and a lot of the adhoc experimental layer combining happening in the wild, (see, e.g. Goliath-120b), I think we're likely to see some pretty interesting architectural improvements this year in the LLM space.
Calm seems to point the way to a next step after MoE, and models like Goliath seem to indicate that even a really really lazy version of Calm (no Linear layer combination, just literally alternating layers at full weights) can be very impactful. Overall I think we will see really, really strong models that are performant on consumer hardware in 2024, likely first half of this year.
Something that has come to light since the release of the weights, and not mentioned in this paper is that it looks like fairly likely that the 8 experts were all seeded by Mistral 7B and subsequently diverged. This has generated a lot of experimentation in the local LLM community with cloning models as a way to cheaply generate experts.
It was generally thought likely that training an 8x7B network would be as much work as training 8 7B networks, but this seems not to have been true for Mistral, which is super interesting.
There's still a lot of rapid innovation happening in this space, with papers like Calm from DeepMind this week, and a lot of the adhoc experimental layer combining happening in the wild, (see, e.g. Goliath-120b), I think we're likely to see some pretty interesting architectural improvements this year in the LLM space.
Calm seems to point the way to a next step after MoE, and models like Goliath seem to indicate that even a really really lazy version of Calm (no Linear layer combination, just literally alternating layers at full weights) can be very impactful. Overall I think we will see really, really strong models that are performant on consumer hardware in 2024, likely first half of this year.