HumanEval is meaningless regardless, those 164 problems have been overfit to the tea.
Hook this up to LLM arena we will get a better picture regarding how powerful they really are
Ahhh that takes me back!
In the keynote, Jensen uses 1.8T in an example and suggests that this is roughly the size of GPT-4 (if I remember correctly).
That might suggest that GPT-4 should be thought of as something like a 250B model. But there's also some selection for the remaining 1/8 of weights that are used by the chosen expert as being the "most useful" weights for that pass (as chosen/defined by the mixture routing), so now it feels like 250B is undercounting the parameter size, whereas 1.8T was overcounting it.
I think it's not really defined how to compare parameter counts with a MoE model.
Similarly, if GPT-4 is really 1.8T you would expect it to produce output of similar quality to a comparable 1.8T model without MoE architecture.
Are you sure about that? I'm pretty sure Miqu (the leaked Mistral 70b model) is generally thought to be smarter than Mixtral 8x7b.
From Databricks: "DBRX has 16 experts and chooses 4, while Mixtral and Grok-1 have 8 experts and choose 2. This provides 65x more possible combinations of experts and we found that this improves model quality. DBRX uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA). It uses the GPT-4 tokenizer as provided in the tiktoken repository. We made these choices based on exhaustive evaluation and scaling experiments."
https://www.databricks.com/blog/introducing-dbrx-new-state-a...
Everyone seems to put each expert on a different GPU in training and inference, so that's how you get to 8 experts, or 7 if you want to put the router on its own GPU too.
You could also do multiples of 8. But from my limited understanding it seems like more experts don't perform better. The main advantage of MoE is the ability to split the model into parts that don't talk to each other, and run these parts in different GPUs or different machines.
1. After ChatGPT release, Twitter spam from influencers about chatGPT is one billion and GPT-4 is 1 trillion.
2. Semianalysis publishes a blog post claiming 1.8T sourced from insiders.
3. The way info diffusion works these days, everyone heard from someone else other than Semianalysis.
4. Up until about a month ago, you could confidently say "hey its just that one blog post" and work through it with people to trace their initial hearing of it back to the post.
5. nVidia press conference some time in the last month used the rumors as an example with "apparently" attached, and now people will tell you NVidia confirmed 1.8 trillion.
my $0.02: I'd bet my life GPT-4 isn't 1.8T, and I very much doubt its over 1 trillion. Like, lightning striking the same person 3 times in the same week.
In general, not someone you wanna be citing with lengthy platitudes, he's an influencer who speaks engineer, he's burned out of every community he's been in, acrimonously.
But it's pretty clear GPT4 Turbo is a smaller and heavily quantized model.