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Who's afraid of Chinese models?

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Re: Who's afraid of Chinese models?

#81

Post by Havoc »

sharadov wrote: post #79

What makes the Chinese models this good? I don't believe it's distillation alone. This from OpenAi's Head of Strategic Futures "Some observations on Kimi: It's a very good model! I don't think its performance can be explained away by distillation or anything like that" https://x.com/deanwball/status/2078133895766114412 China's strategy of spending billions on training these models and open sourcing these models away…

>What makes the Chinese models this good?

Why wouldn't it be? China is pumping out AI research and researchers at a staggering pace and there is no inherent reason why western models should be better

Re: Who's afraid of Chinese models?

#83

Post by nl »

qurren wrote: post #54
_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Government cannot exactly "bar" terms of service. ToS isn't law. The most they can do is say they're unwilling to enforce them. ToS is just conditions that you agree to in order to use a private service that is provided at-will. I can have a private coffee shop where the terms of service are that you must wear red to enter, and if you're not wearing red, you are not welcome on my property. So it would be upto OpenAI…

That's just not true. You can absolutely have terms of service that are illegal, and the government can enforce them.

Re: Who's afraid of Chinese models?

#84

Post by bluegatty »

_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Making an LLM from raw data is value-add.

Distillation is just value extract.

It's soft, and I'm not sure what the answer should be ... but I think that there is a difference.

I think we start by recognizing that ... and then try to figure it out from there.

'The Internet' may be a public good, maybe we make them pay a tax for that, but that's different than distillation.

Re: Who's afraid of Chinese models?

#85

Post by Terr_ »

eli wrote: post #26
_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Seems only fair that if LLMs can use copyrighted data for training then they should be able to use cannot-be-copyrighted output of other LLMs. But barring the terms of service from forbidding distillation seems like a tough sell. OpenAI shouldn't be allowed to decide what types of customers it wants and doesn't want?

> Seems only fair

"You're trying to kidnap what I've rightfully stolen!" -- Vizzini

Re: Who's afraid of Chinese models?

#86

Post by gr_norm »

_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Yup, fair's fair. Anything else stinks of 'rules for thee but not for me' (a maxim the frontier labs seem worryingly happy to apply, on several counts).

Re: Who's afraid of Chinese models?

#87

Post by paxys »

noncoml wrote: post #9
_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Don’t know much about how distillation works so please enlighten me here. > what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models If it’s as easy as that why do they choose to distill another model and not distill the knowledge on the open Internet from scratch?

You need to do both.

A model trained on all knowledge from the internet (and other sources) is large but ultimately not very useful by itself, because it is going to spit out all kinds of garbage. You have to apply multiple further stages of training and refinement to the base model before putting it in front of users. So as an example you can train a model by yourself and then have GPT or Claude continuously check its outputs and correct it when it is wrong, ending up with a far more powerful model.

Re: Who's afraid of Chinese models?

#88

Post by striking »

mediaman wrote: post #62
tristanj wrote: post #29

The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models…

The (quite excellent) article discusses several of your points. If you haven't read it, I recommend it. - Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not. - Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category…

Sure, let's have a look...

> I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. [emphasis mine]

I guess I'm missing the part of this article where they bring hard numbers in to back up the argument here. What work was attempted? https://cursor.com/evals shows the previous generation of open models (Kimi K2.7) trading blows with the others, cost effectively. Composer 2.5 is itself a fine-tune of K2.7, and it's apparently quite token efficient, so why would it be impossible for a Chinese lab to achieve something similar? GLM 5.2 Max is also ranked above the lower end OpenAI models and is not far off in price.

It's weird to have this entire discussion about tokenomics without mention of the circular financing and debt raised by labs in the West, which can then essentially give away their capacity to end users. OpenAI giving away quota resets to subscribers like candy on Halloween while their compute partner Oracle's bonds is reevaluated to be one grade above junk? How?

I don't think you can make an argument about the future one way or another by arguing using the listed prices. The math is not internally consistent enough for it.

Re: Who's afraid of Chinese models?

#89

Post by scotty79 »

bluegatty wrote: post #84
_aavaa_ wrote: post #2

> distillation: why exactly is it bad? After all, what are large language models but the distillation of all of the knowledge on the open Internet, scraped by the frontier labs and distilled into the models that are themselves being distilled? Who is exactly being wronged here? ... The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service th…

Making an LLM from raw data is value-add. Distillation is just value extract. It's soft, and I'm not sure what the answer should be ... but I think that there is a difference. I think we start by recognizing that ... and then try to figure it out from there. 'The Internet' may be a public good, maybe we make them pay a tax for that, but that's different than distillation.

> Making an LLM from raw data is value-add. > Distillation is just value extract.

There is a value-add in selecting the valuable parts out of the garbage. And let's face it. Largest models contain a lot of garbage.

Re: Who's afraid of Chinese models?

#90

Post by nl »

sharadov wrote: post #79

What makes the Chinese models this good? I don't believe it's distillation alone. This from OpenAi's Head of Strategic Futures "Some observations on Kimi: It's a very good model! I don't think its performance can be explained away by distillation or anything like that" https://x.com/deanwball/status/2078133895766114412 China's strategy of spending billions on training these models and open sourcing these models away…

> China's strategy of spending billions on training these models and open sourcing these models away is strategic - they want to kill the US LLM industry at any cost.

Why is it when Anthropic and OpenAI spend billions trying to beat each other it is competition, but when the Chinese companies do it then it is trying to kill the US LLM industry at any cost.

The US federal government spends billions in subsidies via the US Chip Act, and bans chip sales to China to support US companies.

But the implication is that somehow Chinese competition is illegitimate because "strategic".

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