Wow, 128 experts in a single model. That's a lot more than everyone else. The Snowflake team has a blog post explaining why they did that: https://www.snowflake.com/blog/arctic-open-efficient-foundat... But the most interesting aspect about this, for me, is that every tech company seems to be coming out with a free open model claiming to be better than the others at this thing or that thing. The number of choices is…
Snowflake Arctic Instruct (128x3B MoE), largest open source model
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Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#92Earlier quoted context omitted.
Flights from the Western USA to Hawaii are ~2 million tons a year, at least in 2017, wouldn’t be surprised if that number doubled. 500t to train a model at least seems like a more productive use of carbon than spending a few days on the beach. So I don’t think the carbon use of training models is that extreme.
GPT3 was a 175 bln parameters model. All the big boys are now doing trillions of parameters without a substantial chip efficiency increase. So we are talking about thousands of tons of carbon per model, repeated every year or two or however fast they become obsolete. To that we need to add embedded carbon in the entire hardware stack and datacenter, it quickly adds up. If it's just a handfull of companies doing it, f…
It's likely not the model size that's bigger, but the training corpus (see 15T for llama3). I doubt anyone has a model with “trillions” of parameters right now, one trillion maybe as rumored for GPT-4, but even for GPT-4 I'm skeptical about the rumors given the inference cost for super large models and the fact that the biggest lesson we got since llama is that training corpus size alone is enough for performance increase, at a reduce inference cost.
Edit: that doesn't change your underlying argument though: no matter if it's the parameter count that increases while staying at “Chinchilla optimal” level of training, or the training time that increases, there's still a massive increase in training power spent.
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#93Earlier quoted context omitted.
Transformers can definitely solve captchas. Not sure why you think otherwise.
So captchas are obsolete now?
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#94Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#95> What weighs more: a pound of feathers or a great british pound?
> A pound of feathers and a Great British Pound weigh the same, which is one pound.
It works if you add "Think step by step," though.
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#96Reminds of the CPU GHz race. The main thing was that the figures were as large and impressive as possible. The benefit was marginal
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#97Wow, 128 experts in a single model. That's a lot more than everyone else. The Snowflake team has a blog post explaining why they did that: https://www.snowflake.com/blog/arctic-open-efficient-foundat... But the most interesting aspect about this, for me, is that every tech company seems to be coming out with a free open model claiming to be better than the others at this thing or that thing. The number of choices is…
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#98Earlier quoted context omitted.
Looks like they aren't targeting DRGN24 as one of their benchmark suites.
I love getting into arguments with LLMs over whether Worsel is an eastern dragon (in my imagination) or a western dragon (like the bad lensman anime.)
Total aside, but I appreciate your arxiv submissions here. Just because they don't hit the front page, doesn't mean they are seen.
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#99Earlier quoted context omitted.
Yeah but that's a 70B model. You can see on the Inference Efficiency chart that it takes more than 3x as much compute to run it compared to this one.
But you do need to hold all 128 experts in memory? Or not? Or they simply consider inference efficiency as latency
Re: Snowflake Arctic Instruct (128x3B MoE), largest open source model
#100So many models fail at basic reasoning. > What weighs more: a pound of feathers or a great british pound? > A pound of feathers and a Great British Pound weigh the same, which is one pound. It works if you add "Think step by step," though.
Derivations of well known problems trip these models up big time.
Which bullet lands first, a bullet shot horizontally or one shot towards the ground
The bullet shot horizontally will land first, assuming both bullets are fired from the same height and with the same initial velocity. This is because the bullet shot horizontally has a horizontal velocity component that causes it to travel forward while it falls under gravity. The bullet shot towards the ground only has the force of gravity acting on it, causing it to accelerate downwards. Therefore, the bullet shot horizontally will hit the ground later than the one shot towards the ground
It’s not even consistent within the same answer!Even if you ask it to think step by step, it gets confused:
Bullet shot towards the ground: When a bullet is shot towards the ground, it has both an initial horizontal and vertical velocity components. However, since we're only interested in comparing the time it takes for each bullet to reach the ground, we can focus on their vertical velocities and ignore their horizontal velocities.
Let's denote:
( v_y ) as the initial vertical velocity of the second bullet (which is zero in this case)
( t' ) as the time it takes for the second bullet to reach the ground Using the same equation of motion in the vertical direction: [ y = h - \frac{1}{2}g(t')^2 ] Since ( v_y = 0 ), we have: [ t' = \sqrt{\frac{2h}{g}} ]