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QwQ-32B: Embracing the Power of Reinforcement Learning

qwenlm.github.io

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Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#151

Post by h0l0cube »

whatshisface wrote: post #115
h0l0cube wrote: post #97

Earlier quoted context omitted.

Protectionist policy, if applied consistently, will actually lead to more jobs (and higher wages) eventually , but also higher inflation and job losses in the short term, and a more insular economy. It's foolish to go so hard, and so fast – or this is just a negotiation tactic – so I think the Trump administration is going to compromise by necessity, but in time supply chains will adjust to the new reality, and tarif…

That's an assumption, I'm trying to challenge it. Taxes usually take money out of the economy and lead to less activity. Why should a (very high) tax on transportation be different? These are not the sorts of things we can afford to just do without making sure they will work.

> Taxes usually take money out of the economy

This is an oversimplification, they can change incentives, and sometimes increase investment.

> lead to less activity

I do agree money will be divested from the US as they become more and more expensive to deal with (leading to "less activity"), and like I said this will rechannel the economy between the rest of the world. The trade-off is that the US becomes a manufacturer and exporter again (leading to "replaced activity"), some manufacturing capability is duplicated (leading to "more activity" though redundant/less productive), and the currency devalues.

But I'll admit I'm well out of my depth here, and I'm being booed off the stage. All the same, I don't think I'm wrong here. Protectionism isn't new, and lots of countries do it, it's just novel that the world's largest economy and bastion of free trade is doing it to such an extreme.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#152

Post by h0l0cube »

pstuart wrote: post #145
h0l0cube wrote: post #54

Earlier quoted context omitted.

The tarrifs are for the US to build it's own domestic capabilities, but this will ultimately shift the rest of the world's trade away from the US and toward each other. It's a trade-off – no pun intended – between local jobs/national security and downgrading their own economy/geo-political standing/currency. Anyone who's been making financial bets on business as usual for globalization is going to see a bit of a spee…

The tariffs are seen as "free money" that will allow for cutting taxes on the wealthy. Note that the current messaging is "we spend too much money" and there's nothing about "we need to invest in _foo_"

Advanced chip fabs, and battery manufacturers are the first counterexamples that come to mind. The government doesn't need to invest, they just need to watch the free market realign their manufacturing to maintain access to the US economy. Tariffs have been around since Trumps first term, and were retained by Biden, and they're already doing what they intended.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#154

Post by freehorse »

antirez wrote: post #39

Note the massive context length (130k tokens). Also because it would be kinda pointless to generate a long CoT without enough context to contain it and the reply. EDIT: Here we are. My first prompt created a CoT so long that it catastrophically forgot the task (but I don't believe I was near 130k -- using ollama with fp16 model). I asked one of my test questions with a coding question totally unrelated to what it say…

If you did not change the context length, it is certain that it is not 2k or so. In "/show info" there is a field "context length" which is about the model in general, while "num_ctx" under "parameters" is the context length for the specific chat.

I use modelfiles because I only use ollama because it has easy integration with other stuff eg with zed, so this way I can easily choose models with a set context size directly.

Here nothing fancy, just

    FROM qwq
    PARAMETER num_ctx 100000
You save this somewhere as a text file, you run

    ollama create qwq-100k -f path/to/that/modelfile
and you now have "qwq-100k" in your list of models.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#155

Post by pickettd »

seanmcdirmid wrote: post #133
manmal wrote: post #31

I guess I won’t be needing that 512GB M3 Ultra after all.

A max with 64 GB of ram should be able to run this (I hope). I have to wait until an MLX model is available to really evaluate its speed, though.

Looks like MLX quants are released:

- 4bit: https://huggingface.co/mlx-community/QwQ-32B-4bit

- 6bit: https://huggingface.co/mlx-community/QwQ-32B-6bit

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#156

Post by selcuka »

fsndz wrote: post #135
holoduke wrote: post #34

Earlier quoted context omitted.

Have you heard of Jevons paradox? That says that whenever new tech is used to make something more efficient the tech is just upscaled to make the product quality higher. Same here. Deepseek has some algoritmic improvements that reduces resources for the same output quality. But increasig resources (which are available) will increase the quality. There will be always need for more compute. Nvidia is not in trouble. Th…

yeah, sure, I guess the investors selling NVIDIA's stock like crazy know nothing about jevons

> I guess the investors selling NVIDIA's stock like crazy know nothing about jevons

I know you are trying to be sarcastic, but for the sake of argument let's assume that your question is genuine.

There are two types of investors and they both sell, but for different reasons:

1. Casual investors: They don't know much about investing, or Jevons paradox. They only watch the news, so they panic sell.

2. Institutional investors: They know all about Jevons paradox etc, but they also know that casual investors don't, so they sell on purpose so that they can buy the dip later.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#157

Post by dtquad »

gagan2020 wrote: post #5

Chinese strategy is open-source software part and earn on robotics part. And, They are already ahead of everyone in that game. These things are pretty interesting as they are developing. What US will do to retain its power? BTW I am Indian and we are not even in the race as country. :(

>BTW I am Indian and we are not even in the race as country

Why are you surprised?

India was on a per capita basis poorer than sub-Saharan Africa until 2004.

The only reason India is no longer poorer than Africa is because the West (the IMF and World Bank) forced India to do structural reforms in 1991 that stopped the downward trajectory of the Indian economy since its 1947 independence.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#158

Post by k_sze »

antirez wrote: post #39

Note the massive context length (130k tokens). Also because it would be kinda pointless to generate a long CoT without enough context to contain it and the reply. EDIT: Here we are. My first prompt created a CoT so long that it catastrophically forgot the task (but I don't believe I was near 130k -- using ollama with fp16 model). I asked one of my test questions with a coding question totally unrelated to what it say…

Oddly, the Chinese LLM host SiliconFlow only makes it available with 32k context, which is even smaller than their DeepSeek-R1 offering.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#159

Post by rahimnathwani »

Szpadel wrote: post #124
rahimnathwani wrote: post #74

Earlier quoted context omitted.

Is the model using budget forcing?

I do not understand why to force wait when model want to output . why not just decrease probability? if model really wants to finish maybe or could over power it in cases were it's really simple question. and definitely would allow model to express next thought more freely

  why not just decrease  probability?
Huggingface's transformers library supports something similar to this. You set a minimum length, and until that length is reached, the end of sequence token has no chance of being output.

https://github.com/huggingface/transformers/blob/51ed61e2f05...

S1 does something similar to put a lower limit on its reasoning output. End of thinking is represented with the token, followed by the word 'answer'. IIRC the code dynamically adds/removes to the list of suppressed tokens.

Both of these approaches set the probability to zero, not something small like you were suggesting.

Re: QwQ-32B: Embracing the Power of Reinforcement Learning

#160

Post by aurareturn »

dtquad wrote: post #157
gagan2020 wrote: post #5

Chinese strategy is open-source software part and earn on robotics part. And, They are already ahead of everyone in that game. These things are pretty interesting as they are developing. What US will do to retain its power? BTW I am Indian and we are not even in the race as country. :(

>BTW I am Indian and we are not even in the race as country Why are you surprised? India was on a per capita basis poorer than sub-Saharan Africa until 2004. The only reason India is no longer poorer than Africa is because the West (the IMF and World Bank) forced India to do structural reforms in 1991 that stopped the downward trajectory of the Indian economy since its 1947 independence.

  The only reason India is no longer poorer than Africa is because the West (the IMF and World Bank) forced India to do structural reforms in 1991 that stopped the downward trajectory of the Indian economy since its 1947 independence.
India had the world's largest GDP at some point in its history. Why did India lose its status?
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