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Large Enough

mistral.ai

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Re: Large Enough

#51

Post by zainhoda »

Workaccount2 wrote: post #35
Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

I think GPT5 will be the signal of whether or not we have hit a plateau. The space is still rapidly developing, and while large model gains are getting harder to pick apart, there have been enormous gains in the capabilities of light weight models.

I’m waiting for the same signal. There are essentially 2 vastly different states of the world depending on whether GPT-5 is an incremental change vs a step change compared to GPT-4.

Re: Large Enough

#53

Post by genrilz »

Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

For this model, it seems like the point is that it uses way less parameters than at least the large Llama model while having near identical performance. Given how large these models are getting, this is an important thing to do before making performance better again.

Re: Large Enough

#54

Post by Tepix »

drewnick wrote: post #27
tikkun wrote: post #5

Links to chat with models that released this week: Large 2 - https://chat.mistral.ai/chat Llama 3.1 405b - https://www.llama2.ai/ I just tested Mistral Large 2 and Llama 3.1 405b on 5 prompts from my Claude history. I'd rank as: 1. Sonnet 3.5 2. Large 2 and Llama 405b (similar, no clear winner between the two) If you're using Claude, stick with it. My Claude wishlist: 1. Smarter (yes, it's the most intelligent, and y…

All 3 models you ranked cannot get "how many r's are in strawberry?" correct. They all claim 2 r's unless you press them. With all the training data I'm surprised none of them fixed this yet.

LLMs think in tokens, not letters. It's like asking someone who is dyslexic about spelling. Not their strong suit. In practice, it doesn't matter much, does it?

Re: Large Enough

#55

Post by Tepix »

TIPSIO wrote: post #3

This race for the top model is getting wild. Everyone is claiming to one-up each with every version. My experience (benchmarks aside) Claude 3.5 Sonnet absolutely blows everything away. I'm not really sure how to even test/use Mistral or Llama for everyday use though.

Claude is pretty great, but it's lacking the speech recognition and TTS, isn't it?

Re: Large Enough

#56

Post by chipdart »

Workaccount2 wrote: post #35
Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

I think GPT5 will be the signal of whether or not we have hit a plateau. The space is still rapidly developing, and while large model gains are getting harder to pick apart, there have been enormous gains in the capabilities of light weight models.

> I think GPT5 will be the signal of whether or not we have hit a plateau.

I think GPT5 will tell if OpenAI hit a plateau.

Sam Altman has been quoted as claiming "GPT-3 had the intelligence of a toddler, GPT-4 was more similar to a smart high-schooler, and that the next generation will look to have PhD-level intelligence (in certain tasks)"

Notice the high degree of upselling based on vague claims of performance, and the fact that the jump from highschooler to PhD can very well be far less impressive than the jump from toddler to high schooler. In addition, notice the use of weasel words to frame expectations regarding "the next generation" to limit these gains to corner cases.

There's some degree of salesmanship in the way these models are presented, but even between the hyperboles you don't see claims of transformative changes.

Re: Large Enough

#57

Post by eurekin »

majiy wrote: post #42
ActorNightly wrote: post #25

Earlier quoted context omitted.

The thing I don't understand is why everyone is throwing money at LLMs for language, when there are much simpler use cases which are more useful? For example, has anyone ever attempted image -> html/css model? Seems like it be great if I can draw something on a piece of paper and have it generate a website view for me.

That's a thought I had. For example, could a model be trained to take a description, and create a Blender (or whatever other software) model from it? I have no idea how LLMs really work under the hood, so please tell me if this is nonsense.

I'm waiting exactly for this, gpt4 trips up a lot with blender currently (nonsensical order of operations etc.)

Re: Large Enough

#58

Post by jsheard »

talldayo wrote: post #40
llm_nerd wrote: post #23

Earlier quoted context omitted.

Presuming the breakthrough is openly shared. It remains surprising how transparent many of these companies are about new approaches that push the SoTa forward, and I suspect we're going to see a change. That companies won't reveal the secret sauce so readily. e.g. Almost the entire market relies upon Attention Is All You Need paper detailing transformers, and it would be an entirely different market if Google had hel…

Given how absolutely pitiful the proprietary advancements in AI have been, I would posit we have little to worry about.

OTOH the companies who are sharing their breakthroughs openly aren't yet making any money, so something has to give. Their research is currently being bankrolled by investors who assume there will be returns eventually, and eventually can only be kicked down the road for so long.

Re: Large Enough

#59

Post by crowcroft »

breck wrote: post #33

When I see this "© 2024 [Company Name], All rights reserved", it's a tell that the company does not understand how hopelessly behind they are about to be.

Could you elaborate on this? Would love to understand what leads you to this conclusion.

Re: Large Enough

#60

Post by [deleted] »

Liquix wrote: post #10

These companies full of brilliant engineers are throwing millions of dollars in training costs to produce SOTA models that are... "on par with GPT-4o and Claude Opus"? And then the next 2.23% bump will cost another XX million? It seems increasingly apparent that we are reaching the limits of throwing more data at more GPUs; that an ARC prize level breakthrough is needed to move the needle any farther at this point.

[deleted]
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