8 ms·
Qwen3-235B-A22B-Thinking-2507
- sophia01 1y agoIf this is actually competitive with Gemini 2.5 Pro that would be insane esp for an Apache2 truly open weights model, let's hope it's not too hacked to shine on benchmarks!
- lvl155 1y agoQwen3 models are solid and at such a low cost, it doesn’t hurt to pair it with something like Sonnet 4 as a check. I mean it does eliminate a lot of Claude’s “You’re absolutely right!” loops.
- danielhanchen 1y agoI'm making dynamic GGUFs for local inference at https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507-GGUF https://huggingface.co/unsloth/Qwen3-235B-A22B-Thinking-2507... Also guide to run them https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune/qwen3-2507 https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tun...
- arcanemachiner 1y agoWould I notice a difference between the Q2_K and Q2_K_XL variants?
- danielhanchen 1y agoOh I would always use Q2_K_XL :) It uses our dynamic methodology to quantize certain layers in different bits ie 2, 3, 4, 5, 6, 8 bits - the more important the layer is, the higher the bitrate
- Squeeze2664 1y agoHow do you determine the importance of a layer in this case?
- kkzz99 1y agoAfaik they have a test bench that they use and take the activation data from that.
- danielhanchen 1y agoYes we have around 1 to 3 million tokens of high quality self verified data that we use to calibrate models!
- smallerize 1y agohttps://unsloth.ai/blog/dynamic-v2 https://unsloth.ai/blog/dynamic-v2
- danielhanchen 1y agoYes also https://unsloth.ai/blog/deepseekr1-dynamic https://unsloth.ai/blog/deepseekr1-dynamic, https://unsloth.ai/blog/dynamic-4bit https://unsloth.ai/blog/dynamic-4bit, https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs
- tosh 1y agoIf the evals hold up this is a mindblowing new weight to capability ratio edit: afaiu deepseek r1 was 671B with 37B active params
- energy123 1y agoAm I the only one who ignores evals, unless they're holdout datasets like a new IMO competition, or at a minimum evals with a semi-private test set like ARC-AGI 2? How can we trust that these companies don't put copies of these benchmarks in their training data? They can get whatever score they want, up to 100%, easily, by just training on that data sufficiently many times.
- christianqchung 1y agoThere is something of a middle ground here for benchmark skepticism. Big companies wouldn't want a massive divergence between benchmarks and real performance that people could actually notice, and I'd argue for the most part that this hasn't happened too much (although above I posted a problem with Qwen and ARC). However, finetunes by random people/groups don't carry the same downside so I'm basically skeptical of all finetunes before using them for a particular case.
- energy123 1y agoI don't believe these companies see their customers as being able to tell the difference between a real GPQA score and a GPQA score that's fudged upwards by 10%. Look at Elon Musk presenting himself to the world as a Path of Exile expert when in reality he likely hired an expert to level up his account while he himself is an amateur. They think we are idiots and will lie to us to capture market share and lock us into their ecosystem.
- christianqchung 1y agoThat's true, I certainly wouldn't be able to tell. I was thinking on the order of a 20% score vs 70%, but I realize that's not a very compelling range for my point when people are boasting about <5% shifts.
- nonhaver 1y agoimpressive evals. i wonder how much of that can be attributed to the enhanced context understanding. i feel like that/length are the bottleneck of the majority of commercial models.
- Eisenstein 1y agoI don't know, I think that extending context windows is actually detrimental because people assume they can just dump things in there until it fills up. You still have to deal with the limited attention that the models have, and only filling the context with things relevant to the particular thing you are trying to solve is going to be the most effective approach. If you have too much information for it to fit into a 128K window, I think you just have too much information. The entirety of Don Quixote at over 1000 pages is less than 64,000 tokens.
- CamperBob2 1y agoThat sounds low by about 10x, assuming Don Quixote has 430k words (per Google). Still, yes, I don't know of a single model that doesn't go off the rails if you actually try to take advantage of its context length specification.
- Eisenstein 1y agoWell, I loaded up Llama 3 and downloaded the novel, and for the English translation we get 545997 tokens and in the original Spanish 653981 tokens. So when I estimated it did lose a an order of magnitude. Thanks for the correction.
- pama 1y agoAnyone here has tips for the code and hardware setup to get best per-GPU throughput on H200 or B200 hardware for large reasoning traces and inputs of around 10k–40k tokens? Is there an equivalent effort to sglang’s optimization of the V3/R1 throughput for this class of models?
- christianqchung 1y agoFor what it's worth, the Qwen team misreported an ARC-AGI score benchmark on the non-thinking model by a factor of 4, which has not been explained yet. They claimed a score of 41.8% on ARC-AGI 1 [0] which is much higher than what non-chain of thought models have been able to achieve (GPT 4.5 got 10%). The ARC team later benchmarked it at 11%[1], which is still a high score, but not the same as 41.8%. It's still probably a significant update on the model though. [0] https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507 https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507 [1] https://x.com/arcprize/status/1948453132184494471 https://x.com/arcprize/status/1948453132184494471
- ducviet00 1y agoMaybe 41.8% is the score of Qwen3-235B-A22B-Thinking-2507, lol. 11% for the non-thinking model is pretty high
- jug 1y agoMakes sense, it's in line with Gemini 2.5 Pro in that case. It aligns with their other results in the post.
- christianqchung 1y agoThey made it very clear that they were reporting that score for the non-thinking model[0]. I still don't have any guesses as to what happened here, maybe something format related. I can't see a motivation to blatantly lie on a benchmark which would very obviously be publicly corrected. [0] https://x.com/JustinLin610/status/1947836526853034403 https://x.com/JustinLin610/status/1947836526853034403
- mattnewton 1y agoCould it be the public eval set vs the private eval the ARC team has? The public eval set is slightly easier and may have had a some unintentional data leakage since it was released before their training data cutoff.
- coolspot 1y agoThey have provided repro for the 41.8% result here: https://github.com/QwenLM/Qwen3/tree/main/eval https://github.com/QwenLM/Qwen3/tree/main/eval
- Alifatisk 1y agoAlibaba has been on fire lately, do they even sleep?
- esafak 1y agoA rhetorical question, I suppose: https://en.wikipedia.org/wiki/996_working_hour_system https://en.wikipedia.org/wiki/996_working_hour_system
- Der_Einzige 1y agoFolks who know how to train SOTA LLMs dictate their own working conditions. No one is doing 996 there unless they want to.
- donedanadone 1y agoEvals aside why are American labs not able to release open source models at the same speec?
- ttul 1y agoThe Chinese labs can’t compete on inference scale because they have been prevented from accessing the most efficient chips. But since training is a mere fraction of inference these days, they can at least hurt the American companies that are generating billions via inference services. If you can’t beat ‘em, at least pour some sand into their moat, giving China some time to perfect its own nanometer-scale fabrication. It’s a society-wide effort.
- Eisenstein 1y agoThey don't release such huge open weights models because people who run open weights don't have the capability to run them effectively. Instead they concentrate on models like Gemma 3 which goes from 1B to 27B, which when quantized fits perfectly into the VRAM you can get on a consumer GPU.
- regularfry 1y agoThat shouldn't be the case here. Yes, it's memory-bandwidth-limited, but this is an MOE with 22B active. As long as the whole thing fits in RAM, it should be tolerable. It's right at the limit, though.
- deleted 1y ago[deleted]
- lossolo 1y ago> They don't release such huge open weights models because people who run open weights don't have the capability to run them effectively This is a naive take. There are multiple firms that can host these models for you, or you can host them yourself by renting GPUs. Thousands of firms could also host open-source models independently. They don’t release them because they fear competition and losing their competitive advantage. If it weren’t for Chinese companies open-sourcing their models, we’d be limited to using closed-source, proprietary models from the U.S., especially considering the recent LLaMA fiasco.
- adamredwoods 1y agoInteresting, Qwen won't answer specific historical events (Tiananmen Square).
- OldfieldFund 1y agoIt's made by Alibaba :)
- yunohn 1y agoIs it really that interesting to point out for every Chinese oss model release?
- mceachen 1y agoIs it not relevant to reiterate the bias (or lobotomization) for people new to this space?
- lurking_swe 1y agoNo, it’s not really relevant. Should I point out that all the models from providers in the west are very “left-leaning” every time one is released? Is that helpful to the technical discussion, in any way? If you are using an LLM for historical knowledge, questions, or research, then the chinese censorship is relevant. Or for questions about geopolitics.
- mattnewton 1y agoIf you had a specific example where the LLMs showed “left leaning” bias, then yes, it would be interesting to me.
- lurking_swe 1y agoLike i said i normally don’t point this out, but because you asked, here you go: https://www.gsb.stanford.edu/insights/popular-ai-models-show-partisan-bias-when-asked-talk-politics https://www.gsb.stanford.edu/insights/popular-ai-models-show...
- osti 1y agoFor the coding benchmarks, does anyone know what are OJBench and CFEval?
- OldfieldFund 1y agoImpressive evals, but... benchmarks aren't everything. Put this prompt into qwen3-thinking, and then compare with gemini 2.5 pro: --- As candidates for creators, we should first address chaos. What is chaos? If for a given event X in A, all possible events can occur in B, and if such independence is universal, we are faced with chaos. If, however, event X in A limits in some way what can occur in B, a relationship exists between A and B. If X in A limits B unequivocally (we flip a switch, the lamp turns on), the relationship between A and B is deterministic. If X in A limits B in such a way that after X in A, events Y or Z can occur in B, where Y occurs 40 times out of 100 after X in A, while Z occurs 60 times, then the relationship between A and B is probabilistic. --- You have to rewrite the above acting as David Foster Wallace in 2025. Don't mention the year. Make it postmodern. Refer to current and projected events and trends. AI, robotics, etc. you have full creative control. you can make it long if you wish. change every word. make it captivating and witty. You are acting as a demiurge DFW. You need to pass the Turing test here. Sell it to the reader. Write good, high-brow fiction. Avoid phrases that are typical to LLMs/AI writers.
- sophia01 1y agoHave been using this all morning for some integral-heavy math for my PhD (trying to bound certain analytically intractable integrals). It's a bit hit-or-miss. It's been able to come up with some pretty impressive bounds but also feels like more than half it does some really dumb stuff. Compared to Gemini 2.5 Pro it's pretty solid. Its thought traces are really silly though sometimes: it'll pretend to check websites or "pull out a calculator".