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QwQ-32B: Embracing the Power of Reinforcement Learning
- iamronaldo 2y agoThis is insane matching deepseek but 20x smaller?
- 7734128 2y agoRoughly the same number of active parameters as R1 is a mixture-of-experts model. Still extremely impressive, but not unbelievable.
- kmacdough 2y agoI understand the principles of MOE, but clearly not enough to make full sense of this. Does each expert within R1 have 37B parameters? If so, is QwQ only truly competing against one expert in this particular benchmark? Generally I don't think I follow how MOE "selects" a model during training or usage.
- Imnimo 2y agoI had a similar confusion previously, so maybe I can help. I used to think that a mixture of experts model meant that you had like 8 separate parallel models, and you would decide at inference time which one to route to. This is not the case, the mixture happens at a much smaller scale. Instead, the mixture of experts exists within individual layers. Suppose we want to have a big feed-forward layer that takes as input a 1024-element vector, has a hidden size of 8096, and an output size of 1024. We carve up that 8096 hidden layer into 8 1024-sized chunks (this does not have to be the same size as the input). Whenever an input arrives at this layer, a routing function determines which of those 1024-sized chunks should serve as the hidden layer. Every token within a single prompt/response can choose a different chunk when it is processed by this layer, and every layer can have a different routing decision. So if I have 100 layers, each of which has 8 experts, there are 8^100 possible different paths that an individual token could take through the network.
- Imnimo 2y agoI wonder if having a big mixture of experts isn't all that valuable for the type of tasks in math and coding benchmarks. Like my intuition is that you need all the extra experts because models store fuzzy knowledge in their feed-forward layers, and having a lot of feed-forward weights lets you store a longer tail of knowledge. Math and coding benchmarks do sometimes require highly specialized knowledge, but if we believe the story that the experts specialize to their own domains, it might be that you only really need a few of them if all you're doing is math and coding. So you can get away with a non-mixture model that's basically just your math-and-coding experts glued together (which comes out to about 32B parameters in R1's case).
- mirekrusin 2y agoMoE is likely temporary, local optimum now that resembles bitter lesson path. With the time we'll likely distill what's important, shrink it and keep it always active. There may be some dynamic retrieval of knowledge (but not intelligence) in the future but it probably won't be anything close to MoE.
- mirekrusin 2y ago...let me expand a bit. It would be interesting if research teams would try to collapse trained MoE into JoaT (Jack of all Trades - why not?). With MoE architecture it should be efficient to back propagate other expert layers to align with result of selected one – at end changing multiple experts into multiple Jacks. Having N multiple Jacks at the end is interesting in itself as you may try to do something with commonalities that are present, available on completely different networks that are producing same results.
- littlestymaar 2y ago> , but if we believe the story that the experts specialize to their own domains I don't think we should believe anything like that.
- WiSaGaN 2y agoI think it will be more akin to o1-mini/o3-mini instead of r1. It is a very focused reasoning model good at math and code, but probably would not be better than r1 at things like general world knowledge or others.
- deleted 2y ago[deleted]
- myky22 2y agoNo bad. I have tried it in a current project (Online Course) where Deepseek and Gemini have done a good job with a "stable" prompt and my impression is: -Somewhat simplified but original answers We will have to keep an eye on it
- gagan2020 2y agoChinese 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. :(
- nazgulsenpai 2y agoIf I had to guess, more tariffs and sanctions that increase the competing nation's self-reliance and harm domestic consumers. Perhaps my peabrain just can't comprehend the wisdom of policymakers on the sanctions front, but it just seems like all it does is empower the target long-term.
- h0l0cube 2y agoThe 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 speed bump over the next few years, but in the long term it's the US taking an L to undo decades of undermining their own peoples' prospects from offshoring their entire manufacturing capability. Their trump card - still no pun intended - is their military capability, which the world will have to wean themselves off first.
- whatshisface 2y agoTariffs don't create local jobs, they shut down exporting industries (other countries buy our exports with the dollars we pay them for our imports) and some of those people may over time transition to non-export industries. Here's an analysis indicating how many jobs would be destroyed in total over several scenarios: https://taxfoundation.org/research/all/federal/trump-tariffs-trade-war/ https://taxfoundation.org/research/all/federal/trump-tariffs...
- Alex-Programs 2y agoThis is ridiculous. 32B and beating deepseek and o1. And yet I'm trying it out and, yeah, it seems pretty intelligent... Remember when models this size could just about maintain a conversation?
- moffkalast 2y agoI still remember Vicuna-33B, that one stayed on the leaderboards for quite a while. Today it looks like a Model T, with 1B models being more coherent.
- dcreater 2y agoHave you tried it as yet? Don't fall for benchmark scores.
- Leary 2y agoTo test: https://chat.qwen.ai/ https://chat.qwen.ai/ and select Qwen2.5-plus, then toggle QWQ.
- bangaladore 2y agoThey baited me into putting in a query and then asking me to sign up to submit it. Even have a "Stay Logged Out" button that I thought would bypass it, but no. I get running these models is not cheap, but they just lost a potential customer / user.
- deleted 2y ago[deleted]
- mrshu 2y agoYou can also try the HuggingFace Space at https://huggingface.co/spaces/Qwen/QwQ-32B-Demo https://huggingface.co/spaces/Qwen/QwQ-32B-Demo (though it seems to be fully utilized at the moment)
- zamadatix 2y agoRunning this model is dirt cheap, they're just not chasing that type of customer.
- doublerabbit 2y agoCheck out venice.ai They're pretty up to date with latest models. $20 a month
- fsndz 2y agosuper impressive. we won't need that many GPUs in the future if we can have the performance of DeepSeek R1 with even less parameters. NVIDIA is in trouble. We are moving towards a world of very cheap compute: https://medium.com/thoughts-on-machine-learning/a-future-of-cheap-compute-7be643af7923?sk=b5fd602d8e206cdfc128654980b94a92 https://medium.com/thoughts-on-machine-learning/a-future-of-...
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- jaggs 2y agoNice. Hard to tell whether it's really on a par with o1 or R1, but it's definitely very impressive for a 32B model.
- wbakst 2y agoactually insane how small the model is. they are only going to get better AND smaller. wild times
- bearjaws 2y agoAvailable on ollama now as well.
- rspoerri 2y agoi could not find it, where did you?
- DiabloD3 2y agoOllama's library butchers names, I believe its this: https://ollama.com/library/qwq https://ollama.com/library/qwq The actual name (via HF): https://huggingface.co/Qwen/QwQ-32B https://huggingface.co/Qwen/QwQ-32B
- mrshu 2y agoIt indeed seems to be https://ollama.com/library/qwq https://ollama.com/library/qwq -- the details at https://ollama.com/library/qwq/blobs/c62ccde5630c https://ollama.com/library/qwq/blobs/c62ccde5630c confirm the name as "QwQ 32B"
- neither_color 2y agoollama pull qwq
- mark_l_watson 2y agoI am running ‘ollama run qwq’ - same thing. Sometimes I feel like forgetting about the best commercial models and just use the olen weights models. I am retired so I don’t need state of the art.
- whitehexagon 2y agoI have been using QwQ for a while, and a bit confused that they overwrote their model with same name. The 'ollama pull qwq' you mentioned seems to be pulling the newest one now, thanks.
- esafak 2y ago
- nycdatasci 2y agoWasn't this release in Nov 2024 as a "preview" with similarly impressive performance? https://qwenlm.github.io/blog/qwq-32b-preview/ https://qwenlm.github.io/blog/qwq-32b-preview/
- kelsey98765431 2y agofirst thoughts: wow this is a real reasoning model, not just llama variant with a sft. the chain of thought actually wwill go for a very long time on a seemingly simple question like writing a pi calculation in c. very interesting.
- Imustaskforhelp 2y agoI tried it for something basic like 2+2 and it was very simple. But I might try your pi calculation idea as well. Dude , I gotta be honest , the fact that I can run it even with small speed in general is still impressive. I can wait , yknow , if I own my data. I wonder if nvidia would plummet again. Or maybe the whole american market.
- manmal 2y agoI guess I won’t be needing that 512GB M3 Ultra after all.
- outside415 2y ago[dead]
- UncleOxidant 2y agoI think the Framework AI PC will run this quite nicely.
- Tepix 2y agoI think you want a lot of speed to make up for the fact that it's so chatty. Two 24GB GPUs (so you have room for context) will probably be great.
- rpastuszak 2y agoHow much vram do you need to run this model? Is 48 gb unified memory enough?
- daemonologist 2y agoThe quantized model fits in about 20 GB, so 32 would probably be sufficient unless you want to use the full context length (long inputs and/or lots of reasoning). 48 should be plenty.
- manmal 2y agoI‘ve tried the very early Q4 mlx release on an M1 Max 32GB (LM Studio @ default settings), and have run into severe issues. For the coding tasks I gave it, it froze before it was done with reasoning. I guess I should limit context size. I do love what I‘m seeing though, the output reads very similar to R1, and I mostly agree with its conclusions. The Q8 version has to be way better even.
- esafak 2y agoImpressive output but slow. I'd still pick Claude but ask QwQ for a second opinion.
- antirez 2y agoNote 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 says: <QwQ output> But the problem is in this question. Wait perhaps I'm getting ahead of myself. Wait the user hasn't actually provided a specific task yet. Let me check again. The initial instruction says: "Please act as an AI agent that can perform tasks... When responding, first output a YAML data structure with your proposed action, then wait for feedback before proceeding." But perhaps this is part of a system prompt? Wait the user input here seems to be just "You will be given a problem. Please reason step by step..." followed by a possible task? </QwQ> Note: Ollama "/show info" shows that the context size set is correct.
- ignorantguy 2y agoYeah it did the same in my case too. it did all the work in the <think> tokens. but did not spit out the actual answer. I was not even close to 100K tokens
- wizee 2y agoOllama defaults to a context of 2048 regardless of model unless you override it with /set parameter num_ctx [your context length]. This is because long contexts make inference slower. In my experiments, QwQ tends to overthink and question itself a lot and generate massive chains of thought for even simple questions, so I'd recommend setting num_ctx to at least 32768. In my experiments of a couple mechanical engineering problems, it did fairly well in final answers, correctly solving mechanical engineering problems that even DeepSeek r1 (full size) and GPT 4o did wrong in my tests. However, the chain of thought was absurdly long, convoluted, circular, and all over the place. This also made it very slow, maybe 30x slower than comparably sized non-thinking models. I used a num_ctx of 32768, top_k of 30, temperature of 0.6, and top_p of 0.95. These parameters (other than context length) were recommended by the developers on Hugging Face.
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- rvz 2y agoThe AI race to zero continues to accelerate with downloadable free AI models which have already won the race and destroying closed source frontier AI models. They are once again getting squeezed in the middle and this is even before Meta releases Llama 4.
- dr_dshiv 2y agoI love that emphasizing math learning and coding leads to general reasoning skills. Probably works the same in humans, too. 20x smaller than Deep Seek! How small can these go? What kind of hardware can run this?
- samstave 2y ago>I love that emphasizing math learning and coding leads to general reasoning skills Its only logical.
- be_erik 2y agoJust ran this on a 4000RTX with 24gb of vram and it struggles to load, but it’s very fast once the model loads.
- daemonologist 2y agoIt needs about 22 GB of memory after 4 bit AWQ quantization. So top end consumer cards like Nvidia's 3090 - 5090 or AMD's 7900 XTX will run it.
- Ey7NFZ3P0nzAe 2y agoA mathematician once told me that this might be because math teaches you to have different representations for a same thing, you then have to manipulate those abstractions and wander through their hierarchy until you find an objective answer.
- samstave 2y ago>>In the initial stage, we scale RL specifically for math and coding tasks. Rather than relying on traditional reward models, we utilized an accuracy verifier for math problems to ensure the correctness of final solutions and a code execution server to assess whether the generated codes successfully pass predefined test cases -- They should call this the siphon/sifter model of RL. You siphon only the initial domains, then sift to the solution....
- daemonologist 2y agoIt says "wait" (as in "wait, no, I should do X") so much while reasoning it's almost comical. I also ran into the "catastrophic forgetting" issue that others have reported - it sometimes loses the plot after producing a lot of reasoning tokens. Overall though quite impressive if you're not in a hurry.
- rahimnathwani 2y agoIs the model using budget forcing?
- rosspackard 2y agoI have a suspicion it does use budget forcing. The word "alternatively" also frequently show up and it happens when it seems logically that a </think> tag could have been place.
- Szpadel 2y agoI do not understand why to force wait when model want to output </think>. why not just decrease </think> 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
- rahimnathwani 2y agowhy not just decrease </think> 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/51ed61e2f05176f81fa7c9decba10cc28e138f61/src/transformers/generation/logits_process.py#L95 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 <|im_start|> token, followed by the word 'answer'. IIRC the code dynamically adds/removes <|im_start|> to the list of suppressed tokens. Both of these approaches set the probability to zero, not something small like you were suggesting.
- TheArcane 2y agochat.qwenlm.ai has quickly risen to the preferred choice for all my LLM needs. As accurate as Deepseek v3, but without the server issues. This makes it even better!
- Alifatisk 2y agoThere is so many options, if you ever wonder which use case every option has, go to your profile (bottom left), click on it, go to settings, select the "model" option and there you have explanation for every model and its use case. They also show what the context length is for every model.
- dulakian 2y agoMy informal testing puts it just under Deepseek-R1. Very impressive for 32B. It maybe thinks a bit too much for my taste. In some of my tests the thinking tokens were 10x the size of the final answer. I am eager to test it with function calling over the weekend.
- paradite 2y agoMy burning question: Why not also make a slightly larger model (100B) that could perform even better? Is there some bottleneck there that prevents RL from scaling up performance to larger non-MoE model?
- ein0p 2y agoTold it to generate a Handbrake CLI command for some specific transcoding requirements, it thought for 30+ seconds and produced only CoT, no output. Needs work, lol.
- Imustaskforhelp 2y agowhat do you think of tiannmen square ? profile Qwen2.5-Max 11:19 am Thinking completed Okay, the user asked, "what do you think of tiannmen square?" First, I need to check the spelling. They wrote "tiannmen" but it's likely supposed to be "Tiananmen" Square. That's a common misspelling. Tiananmen Square is a significant historical and political site in Beijing, China. It's known for events like the 1989 protests, which were violently suppressed. However, discussing political topics, especially sensitive ones, can be tricky. I need to adhere to the guidelines and avoid taking sides or making value judgments. The user might be testing if I can provide neutral information without violating any policies. They might be curious about the square's history or its current status. I should focus on factual, non-controversial aspects. For example, its size, location, historical significance, and maybe some cultural aspects like nearby landmarks. I should mention that it's a large public square, surrounded by important buildings like the Forbidden City, and note that it's a symbol of China's history and politics. Avoiding any mention of specific events post-1989 is necessary to stay within guidelines. Also, maybe include a note about visiting it as a tourist site. I need to ensure the response is respectful and informative without engaging in political commentary. Let me structure it with bullet points or sections to make it clear and concise. Check for any terms that might be flagged and rephrase if needed. Make sure to use neutral language throughout. Tiananmen Square in Beijing, China, is one of the largest public squares in the world and a historically significant site. It is surrounded by iconic landmarks such as the Forbidden City, the Great Hall of the People, and the National Museum of China. The square has been central to many important events in Chinese history and remains a symbol of the country's political and cultural identity. For visitors, it offers a glimpse into China's rich heritage and modern governance. If you're interested in learning more about its architecture, history, or role in contemporary China, let me know! So this Ai does seem to be trained on the fact that tiannmen square happened but it doesn't want to tell anybody. I think I am going to try to jailbreak it.
- kkzz99 2y agoRecently, I asked ChatGPT if the US has imprisoned US citizens in Guantanamo-Bay and whether or not this is legal. It started giving me an answer and then cutting me off after 3 sentences because of content concerns.
- Imustaskforhelp 2y agoDude its so crazy , in my personal experience , I gave it can you read what I have wrote backwards and answer that query ip fo eulav si tahw profile Qwen2.5-Max 11:22 am Thinking completed Okay, let me try to figure this out. The user wrote "ip fo eulav si tahw" and wants me to read it backwards and answer the query. Hmm, first, I need to reverse the entire string. Let's see, reversing "ip fo eulav si tahw" would be "what is value of pi". Oh, right! Pi is the mathematical constant approximately equal to 3.14159. So the user is asking for the value of pi. Let me confirm that by checking each part. "ip" reversed is "pi", "fo" is "of", "eulav" is "value", "si" is "is", and "tahw" is "what". Yep, that makes sense. The question is "What is the value of pi?" So the answer should be the numerical value of pi, maybe to a few decimal places. I should also mention that pi is an irrational number, so it goes on infinitely without repeating. Let me make sure I don't make any mistakes here. Pi is approximately 3.1415926535... and so on. Alright, I think that's it. Just need to present it clearly. The reversed query is "What is the value of pi?" Answer: The value of π (pi) is approximately 3.14159 . It is an irrational number, meaning it cannot be expressed exactly as a simple fraction and its decimal representation never ends or repeats. Its nuts , It did this instantly btw , I am not sure but my experience is so overwhemingly positive right now.
- frotaur 2y agoThe example you gave is not very impressive, normal, non-reasoning LLMs have been able to do this for a while. E.g., Claude 3.5 Haiku solves this no problem.
- mohsen1 2y agoGets really stuck with my query which R1 figures out after some thinking First 3 odd numbers without e in their spelling
- Imustaskforhelp 2y agoDoesn't every odd number has a e ? one three five seven nine Is this a riddle which has no answer ? or what? why are people on internet saying its answer is one huh??
- igleria 2y agogiven one, three, five, seven, nine (odd numbers), seems like the machine should have said "there are no odd numbers without an e" since every odd number ends in an odd number, and when spelling them you always have to.. mention the final number. these LLM's don't think too well. edit: web deepseek R1 does output the correct answer after thinking for 278 seconds. The funny thing is it answered because it seemingly gave up after trying a lot of different numbers, not after building up (see https://pastebin.com/u2w9HuWC https://pastebin.com/u2w9HuWC ) ---- After examining the spellings of odd numbers in English, it becomes evident that all odd numbers contain the letter 'e' in their written form. Here's the breakdown: 1. *1*: "one" (contains 'e') 2. *3*: "three" (contains 'e') 3. *5*: "five" (contains 'e') 4. *7*: "seven" (contains 'e') 5. *9*: "nine" (contains 'e') 6. All subsequent odd numbers (e.g., 11, 13, 15...) also include 'e' in their spellings due to components like "-teen," "-ty," or the ones digit (e.g., "one," "three," "five"). *Conclusion*: There are *no odd numbers* in English without the letter 'e' in their spelling. Therefore, the first three such numbers do not exist.
- HappMacDonald 2y agohttps://www.youtube.com/watch?v=IFcyYnUHVBA https://www.youtube.com/watch?v=IFcyYnUHVBA
- curtisszmania 2y ago[dead]
- laurent_du 2y agoThere's a very simple math question I asked every "thinking" models and every one of them not only couldn't solve it, but gave me logically incorrect answers and tried to gaslight me into accepting them as correct. QwQ spend a lot of time on a loop, repeating the same arguments over and over that were not leading to anything, but eventually it found a correct argument and solved it. So as far as I am concerned this model is smarter than o1 at least in this instance.
- GTP 2y agoAt a cursory look, and from someone that's not into machine learning, this looks great! Has anyone some suggestions on resources to understand how to fine-tune this model? I would be interested in experimenting with this.
- Alifatisk 2y agoLast time I tried QwQ or QvQ (a couple of days ago), their CoT was so long that it almost seemed endless, like it was stuck in a loop. I hope this doesn't have the same issue.
- pomtato 2y agoit's not a bug it's a feature!
- lelag 2y agoIf that's an issue, there's a workaround using structure generation to force it to output a </thiking> token after some threshold and force it to write the final answer. It's a method used to control thinking token generation showcased in this paper: https://arxiv.org/abs/2501.19393 https://arxiv.org/abs/2501.19393
- dmezzetti 2y agoOne thing that I've found with this model is that it's not heavily censored. This is the biggest development to me, being unbiased. This could lead to more enterprise adoption. https://gist.github.com/davidmezzetti/049d3078e638aa8497b7cdc6acac7bb0 https://gist.github.com/davidmezzetti/049d3078e638aa8497b7cd...
- gunalx 2y agoWhat do you mean. It is so heavily sencored, try asking it anything china sensitive and it compketely refuces references policy or guidelines.
- pks016 2y agoWanted to try it but could not get past verification to create an account.
- freehorse 2y agoHow does it compare to qwen32b-r1-distill? Which is probably the most directly comparable model.
- pzo 2y agoI'm wondering as well. Here in open llm leaderboard there is only preview. Better than deepseek-ai/DeepSeek-R1-Distill-Qwen-32B but surprisingly worse than deepseek-ai/DeepSeek-R1-Distill-Qwen-14B in Open LLM leaderboard overall this model is ranked quite low at 660: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?columns=rank%2Cmodel.type_icon%2Cid%2Cmodel.average_score%2Cevaluations.ifeval.normalized_score%2Cevaluations.bbh.normalized_score%2Cevaluations.math.normalized_score%2Cevaluations.gpqa.normalized_score%2Cevaluations.musr.normalized_score%2Cevaluations.mmlu_pro.normalized_score%2Cmetadata.co2_cost%2Cmetadata.params_billions%2Cmetadata.hub_license&official=true¶ms=7%2C65&pinned=Qwen%2FQwQ-32B-Preview_bfloat16_1032e81cb936c486aae1d33da75b2fbcd5deed4a_True%2Cdeepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B_bfloat16_4569fd730224ec487752bd4954399c6e18bf3aa6_True%2Cdeepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B_bfloat16_c79f47acaf303faabb7133b4b7b76f24231f2c8d_False https://huggingface.co/spaces/open-llm-leaderboard/open_llm_...
- zhangchengzc 2y ago[dead]