🚀 Qwen3-4B-Thinking-2507 released!

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Int2float@reddit

Does fine-tuning of thinking vlm model different from normal vlm ?

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ILoveMy2Balls@reddit

Did I read that right? 256k context window in a 4b model? Just amazing

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LeonJones@reddit

Don't know much about LLMs. Why is this significant?

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So, basically a 4b, or 4 billion parameter model is quite small compared to the models you would use on a platform like ChatGPT or Gemini / etc, which are 200+b parameters. Those large models are huge in size and need a much more massive amount of memory and compute power to run, which prevents consumers generally from being able to run them locally. A 4b parameter model is in the size range that it could be fit into and run on a device with the specs of a smartphone. Now, for the other part - context window - 250k tokens, or 250,000 tokens of context window means it can consider approximately that many words (a token is how LLMs consume / measure input and are equivalent to actually roughly 0.75 English words on average). Once a chat thread has more than the context window of tokens in it exceeded, either through conversation continuing or if the initial request is large, the LLM will have holes in the information it can consider and will get / become confused. Anyway, that user is saying that 250k tokens is a large sized context window for such a small model. Hope this helps! :)

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Nikilite_official@reddit

i'm saving this comment!

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shark8866@reddit

would a 30b model fit on a mid laptop?

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para2para@reddit

I asked GPT-5: [https://chatgpt.com/share/6898c3ed-1184-8011-b20e-552ce2661c17](https://chatgpt.com/share/6898c3ed-1184-8011-b20e-552ce2661c17)

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ForsookComparison@reddit

My phone can finally read entire ebooks for me

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richardstevenhack@reddit

Well, I just tried it with my standard basic query: "How many moons does Mars have?" This was the response before I hit "stop generating": \*\*:Qwen3-4B-Thinking-2507-F16-1754530927314:latest:\*\* What are their names? Okay, the user is asking how many moons Mars has and what they're called. Let me start by recalling basic astronomy facts. I remember that Mars has two small moons. Hmm, I think their names are Phobos and Deimos. But wait, let me double-check to be sure. Sometimes people confuse them with Jupiter's moons or other planets' satellites. Yeah, definitely Phobos and Deimos for Mars. The user might be a student working on homework, or maybe just someone curious about space. They probably need the names spelled correctly and maybe a bit of context. Since they asked "how many," I should state that clearly upfront—two moons. I wonder if they know these moons are irregular in shape compared to Earth's Moon. Maybe adding a brief note about their size or orbital characteristics would be helpful, but only if it doesn't complicate things too much. The question is straightforward though: number and names. Also, should I mention that Phobos orbits closer and has a short period? Or maybe just stick to the basics unless they ask for more details. The user didn't specify depth, so keeping it simple is better. Wait, are there any common misconceptions here? Like some people think Mars has no moons or that it's like Venus with none. But no, we know it has two. Better clarify that upfront to avoid confusion. I should present the answer clearly: "Mars has two moons named Phobos and Deimos." Then maybe add a short sentence about their origins—like how they're thought to be captured asteroids—to give context without overloading. But since the question is direct, I'll keep it concise unless the user follows up. Also, spelling matters. Phobos (not "Phobus" or other variants) and Deimos (sometimes misspelled as "Deimos" but that's correct). Better confirm the spelling in my knowledge base—yes, standard is Phobos and Deimos. The user might appreciate knowing they're named after Martian myths: Phobos means fear (from Greek for dread), Deimos means terror. That adds a nice touch without being too technical. But again, only if it's relevant to their query. Since the question is "what are their names," including the mythological origin could be a helpful extra detail. I'll structure the response as: 1. Direct answer (two moons) 2. Names with spelling 3. Brief context about origins (optional but useful) 4. Avoid jargon to keep it accessible No need for complex orbital data unless asked. Keep it friendly and precise. </thinking> And it went ON AND ON until I hit "stop generating". It never stopped looping in its "thinking". This is why I never use thinking models. They DO NOT know when to stop.

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Party-Ingenuity-3967@reddit

The model starting with "What are their names?" screams wrong chat template.

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redditemailorusernam@reddit

This model is smart and small, but it takes \~two minutes to generate an answer. It thinks for ages. Makes it not that useful for help with coding. But is it because I have GeForce RTX 3060 GPU? Would a modern graphics card give me an answer in like 1 second?

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nore_se_kra@reddit

So we have these small models that are fast too but then take ages due to giant thinking context? Additionally they are actually not so small anymore if you have to put those huge contextes in ram too. The trade off is really hard to grasp. At least it would easily run in vlllm....

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RMCPhoto@reddit

Yes, you're absolutely right. Frankly I'm not a fan of this either, ie I don't think the newest deepseek revision was worth the token cost. But check out non thinking models trained for function calling if you want efficiency - this is much closer to a router type model used in agent scenarios: [https://huggingface.co/watt-ai/watt-tool-8B](https://huggingface.co/watt-ai/watt-tool-8B) In my mind, the whole point of the expansion into "agents" is to enable multi-model systems where the most efficient tool for the job is used.

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sonicnerd14@reddit

This is exactly the use case for these smaller models. Think multiple gpus, each filled with multiple models across their vram, assigned to multiple agents that specialize in specific tasks, all dynamically working together. Ultimately, this is a system we are talking about now, and no longer just a question on prowess of individual models.

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RMCPhoto@reddit

100% That is the future. I see so many rebarded takes on the daily that don't take this into account and havent even though it's clearly the roadmap. It's just going to take time to optimize a system like this. But it is the way, for both cost reduction (primary driver for industry - see GPT-5), and for the ability to tune each part of the system independently.... which is much better... Like... a billion times better lol. It's why gorilla could beat GPT4 in function call ing "way back when". The problem I see is that there hasn't been a consolidation around a framework or methodology to accomplish this. MCP's gets us somewhat there. But we need a bit more of an "agent" framework that's closer to metal and is a bit beyond "agent" and supports a more general concept of networking. Take away: \- smaller narrower models will always be better and more efficient at specific tasks. \- find high volume % specific tasks (we have plenty) \- find the smallest model that via training with task specific data performs at the target success rate. \- Need for a framework we agree on

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no_witty_username@reddit

Tradeoff is that you can put the small models on your device that has limited vram or ram, so where as before you have 0 llms running on device, now you will have 1 slow running llm. Better then nothing. Many tasks might also not need to be latency dependent and so opens up opportunities for interesting options

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nore_se_kra@reddit

As long as the context isnt getting too big. I was just testing this model with q4 and after thinking for 10k tokens its was so confused it stopped thinking with </thinking> instead of </think> in one case.

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InsideYork@reddit

What are you talking about? QWQ wasn’t even a year ago.

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RMCPhoto@reddit

He means that sometimes it's better to use a 8b model that can get to the right answer much faster. Long long chain reasoning is an inherent problem with reinforcement learning if not tuned correctly. You can let the reinforcement learning cook forever and the reasoning ends up getting longer and longer on average. You can see deepseek did the same thing. A lot of Qwen models are falling into this trap. It makes them look great on benchmarks though.

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InsideYork@reddit

I suppose the question is why bother with small thinking models anyway? I see qwen3 4b as a refinement of qwq to be smaller faster and probably worse.

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EstarriolOfTheEast@reddit

Small thinking models don't actually need to think for near as long as they do to significantly gain in accuracy. This model will be perfect for experimentation on how to guide reasoning in LLMs to control length vs accuracy without any cut offs. Reasoning in context also allows for classes of computations in the 4B that simply scaling up to 8B or even 14B won't allow.

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RMCPhoto@reddit

Small thinking models are really only good in the areas they've received explicit reinforcement learning in. They don't generalize very well. Which is fine. But they should be targeted at a limited number of use cases. Small general models are no good. Small narrow models can be amazing.

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EstarriolOfTheEast@reddit

I agree that generalization is not a strong point of small LLMs (especially), however they are still quite general from an NLP perspective. And the examples they train on provide suitable coverage for a good number of practical problems. Non-thinking small LLMs are insufficiently reliable, but in theory, training them to reason actually raises the complexity class of their expressible programs to a level that scale alone will not so easily be able to brute force. I'd given up experimenting with small LLMs, but with the new 30 and 20B MoEs and this model, it seems time to dive back in!

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InsideYork@reddit

The problem is that they overthink especially if they don't have tools and get the wrong answer.

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EstarriolOfTheEast@reddit

I believe methods to actively control the reasoning process in open LLMs is under-explored. Including tool access and length control, it's worth seeing how far these models can be pushed.

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FluoroquinolonesKill@reddit

You’re allowed to use a smaller context.

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nore_se_kra@reddit

Yes but then the thinking gets cut off if you're unlucky and you have to retry (automatically). Sometimes its worth it but if its happening too often, you're waisting time with runs you cant use.

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Few_Painter_5588@reddit

More evidence that Hybrid Reasoning hurts the performance of an LLM.

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InsideYork@reddit

Im sure that’s why oai released a hybrid.

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chisleu@reddit

Glm 4.5 is fucking good

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PackAccomplished5777@reddit

gpt-oss is **not** a hybrid model, it always has reasoning enabled (even if it's short with low/medium reasoning effort). Sure you can disable reasoning if you force it, but it's not meant to be used in that way.

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randomstuffpye@reddit

People seem to be saying the 12b oss model sucks. is it meant to be used in a different way? Genuinely curious

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InsideYork@reddit

Qwen3 did this and they were hybrid models.

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PackAccomplished5777@reddit

A hybrid model is one that was explicitly trained to be used in both reasoning and non-reasoning modes, and that the model creator explicitly mentions that. OpenAI doesn't say that their models are "hybrid" anywhere, and the lowest reasoning effort they show is "low", not "disabled". So by a logical conclusion they're not reasoning models.

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InsideYork@reddit

Who defines it that way? Why is that distinction important? Qwen's models worked the same and the end result is the same, a worse model for both.

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mikael110@reddit

I don't know anybody who defines it differently from what the above poster describes. And how else would you define a Hybrid model anyway? If you define Hybrid as any thinking model where it is at all possible to force it to not think, then literally all open thinking models would qualify, including this new release from Qwen. It's not that complicated. First we have traditional LLMs that were not trained with reasoning in mind (though they were still perfectly capable of things like CoT if prompted to do so), then we had models trained specifically to reason, like R1, where yes, you could force them to not think, but doing so was not remotely a recommended or supported use case. Then we quite recently have had a number of models that were explicitly trained to operate in both modes. Which includes models like Sonnet 4, Qwen's older models, and so on. Its been shown pretty well at this point that the Hybrid strategy is not really working out well. And now labs are rowing back from it.

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InsideYork@reddit

Qwen definitely defined it different. https://qwenlm.github.io/blog/qwen3/

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SociallyButterflying@reddit

Its so lobotomized I don't think it would have mattered either way

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Sidran@reddit

It's not lobotomized, it's just deeply respectful of boundaries you didn't know you had.

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taariqlewis@reddit

I feel so much better now. Thank you!

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DangKilla@reddit

*User said thank you. Maybe they're depressed. Cheer them up!* You're so welcome! ☺️ Please let me know if you need a joke or anything else!

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xAragon_@reddit

Maybe it's better, but then you'd have to pick a different model for different prompts. That won't really work for general LLMs that sometimes get requests that benefit from thinking, and sometimes don't, like Gemini, Claude, etc.

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vibjelo@reddit

> Maybe it's better, but then you'd have to pick a different model for different prompts. This is pretty much what you have to do today regardless, no model is the best at everything, they're all "the best" at some section of stuff you'd give an LLM. Especially in a professional context where you need the accuracy to be way above what most people seem OK with in a personal context.

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xAragon_@reddit

Maybe you do, most people don't switch models. Thwy just use ChatGPT / Gemini with thw default settings.

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randomstuffpye@reddit

Been trying to find a solution for this with a simple ui. so far I’m chaining together some mcps and proxy’s for enhanced communication

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LetterRip@reddit

Gemini has three models flash a non thinking model, pro a thinking model, and pro deep thinking. The users picks which based on the task.

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xAragon_@reddit

There is no Gemini Pro / Flash non-thinking, and these are their two most used models.

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LetterRip@reddit

Gemini Flash doesn't enable thinking via the Chat-interface (you can enable it if using the API or via studio). Only the pro-model does thinking via the chat interface. I suppose you can call it a 'thinking model' but if that feature is disabled, it is a 'thinking model' with a thinking budget of 0.

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randomstuffpye@reddit

Holy! Thanks for the tip!

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Fox-Lopsided@reddit

There is a Flash non thinking but not a pro Version

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BlueSwordM@reddit

There is Gemini Flash 2.5 Non Thinking though.

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InsideYork@reddit

The cost of tuning an older LLM is currently higher than using a newly tuned released one. Agentic is where it’s headed so I think smaller is going to get ~~bigger🤣~~.

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Q_H_Chu@reddit

Sorry newbie here, but what is the Hybrid Reasoning model? And how its different from normal LLM ?

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Jan49_@reddit

Hybrid reasoning model = You can enable/disable thinking based on prompt. It was "trained" into the LLM. But we found out, that this hurts the intelligence of the model

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Q_H_Chu@reddit

Yeah, it sounds more like memory-intensive SFT

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nialv7@reddit

Makes you wonder why is that

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FullOf_Bad_Ideas@reddit

That's a result of their GSPO optimization, not pure toggle'able reasoning.

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AI-On-A-Dime@reddit

Yup that’s why everyone releases one of each. But never say never i guess. We need to crack the code somewhere along the line if we ever wanna go AGI.

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da_grt_aru@reddit

The GPQA and AIME25 benchmarks alone out it's logical IQ somewhere at 150+ range compared to human performance! Factor in no-fatigue and lack of bias and emotions and you got yourself a very smart friend in your laptop!

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Ok_Librarian_7841@reddit

Is there any benchmarks for it against Gemma3n4b ?

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RMCPhoto@reddit

They have completely different target use-cases despite being the same size. This is really going to be primarily a tool calling model where the optimization is more about pathfinding. Gemma 3n is designed to be more of a generative / data extraction translation type model. I wouldn't weigh them side by side. Plus, google's tool calling is some of the worst. 2.5 pro ranks like 40th on bfcl.

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Miserable-Dare5090@reddit

Gemma 3n is larger. It has 4B active params, but it is 8B or so total.

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CosmosisQ@reddit

...what? Benchmarking the models against each other across a variety of specialized tasks would be a great way to quantitatively highlight their specialization.

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Voxandr@reddit

Can we benchmark against gpt-oss-20b ? I think this would surpass that 20b.

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entsnack@reddit

gpt-oss-120b destroys this on AIME2025 without tools, it's dead on arrival.

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PermanentLiminality@reddit

It is a 4B model. Are you really expecting it to be better than a 120B model? I'm playing with it now. It does surprisingly well. It may be my new autocomplete model.

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sabertooth9@reddit

Can this be used in vs code for auto completion?

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PermanentLiminality@reddit

That is what I'm going to try. I use [continue.dev](http://continue.dev) in vscode.

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KnifeFed@reddit

It's been 25 minutes, how'd it go??

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PermanentLiminality@reddit

Unfortunately, I had go back to work :( Darn meeting...

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sabertooth9@reddit

Keep us updated ^^

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PermanentLiminality@reddit

It works OK, but the thinking makes it too slow I think. Holding out for a 7B dense model to replace the qwen 2.5 coder 7b I currently use.

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sabertooth9@reddit

Did you tried out other qwen 3 models, socially coder models for completion

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entsnack@reddit

typo, I mean gpt-oss-20b. The gpt-oss models are MoE models with just 3.6B and 5.1B active parameters.

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ExchangeBitter7091@reddit

20B is still more than 5 times bigger than a 4B model. Also, GPT OOS 20B thinks a LOT in high reasoning mode and it actually looses by 10% in a medium reasoning mode. Not sure about Qwen3 4B, but I'd guess it would think a bit less, as 30B A3B 2507 was quite ok in terms of reasoning length

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InsideYork@reddit

I am because that model came from OpenAI. I bet this 4B would trounce it at some things because they censored it that bad.

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Voxandr@reddit

4B, Destroyed by 120B Dead on Arrival Are you a bot from OpenAI?

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adel_b@reddit

it would surpass that 120b

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i-exist-man@reddit

now I am excited

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Trick-Independent469@reddit

why would you benchmark against trash ?

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Voxandr@reddit

Just to troll Scamaltman more

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arthursucks@reddit

I know it's anecdotal, but I just got better HTML output from this tiny model than I got from the GPT-OSS-20.

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Smile_Clown@reddit

>I know it's anecdotal, it is, I do not care to farm karma. GPT-OSS-20 refactored a very large project I had flawlessly. So, you know, just a comment lost in the "OpenAI always sucks no matter what cause the internet told me so" crowd.

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agentspanda@reddit

It’s a little wild how much the internet will jerk off Qwen no matter what and shits on OpenAI even when they have a great platform or model. I don’t have a horse in the race but it becomes scary how propagandized the tribalism is here.

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pneuny@reddit

People here simply don't like what OpenAI represents. Centralized control of AI. Even if they make well functioning (at allowed tasks under ideal conditions) open weights models, if they fail at benchmarks that measure sovereign usage of AI, then they fail to meet the localllama philosophy, even if it can run locally.

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agentspanda@reddit

But that’s clearly not what’s going on- ask Qwen3 235B about Tiananmen Square and it’ll give you a censored silly answer about “mischaracterized events in western media”. So clearly the issue isn’t that people don’t trust models with tokens yanked out of its “brain”, they’re just fine with Qwen, and are fine with centralized control of AI as long as it’s… not American? That’s just backwards.

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Useful_Rice5993@reddit

I dislike them too, but I don't want people to shit up all discourse like pests for it. Having no fucking discrimination is a good generalisation for reddit (and et al), it only harms and should be discouraged.

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billiebol@reddit

I have a specific language-based task that needs to be performed on a device. What is the best way to fine-tune it?

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Whole-Assignment6240@reddit

Really impressive progress — especially curious to see how the "increased thinking length" plays out in real-world use.

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masc98@reddit

unsloth support when? ❤️

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GabryIta@reddit

Now: [https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF)

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mvchamp@reddit

It's out. [https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF](https://huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF)

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sig_kill@reddit

What are the advantages of their fine tuning?

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InsideYork@reddit

Trustworthy results with their dynamic quants and you can train it on your own hardware too. /r/unsloth

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milo-75@reddit

Did I miss the VL model update. That’s what I’m really waiting for!

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V0dros@reddit

They haven't released it yet [https://x.com/JustinLin610/status/1952403331676246022](https://x.com/JustinLin610/status/1952403331676246022)

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nmkd@reddit

Same. There's no Qwen 3 VL (yet?)

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milo-75@reddit

Qwen 3 Omni would also be awesome. If there was a good 50B param model that was multimodal in both directions, then we’d really be cooking.

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trumpdesantis@reddit

How do I access this?

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Sasikuttan2163@reddit

Amazing how Qwen team just keeps pushing amazing work for smaller models. Just last week we switched from Qwen3 32B to Qwen3-30BA3B-2507 at work, very impressed.

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TacGibs@reddit

How is it going so far ? What are the differences in your application ? Thanks !

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Sasikuttan2163@reddit

Pretty good Id say! The A3B model is quite a lot faster than 32B which we used to have earlier, and is less resource intensive at the same time.

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themadman0187@reddit

What are some vague use cases for your model at work?

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Sasikuttan2163@reddit

Mostly synthetic dataset generation

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foldl-li@reddit

It makes me happy to be still in July.

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stumblinbear@reddit

I said "Hello" to it and it immediately responded in Chinese. Remarkable. We've come so far

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schorhr@reddit

Neat! One of the few smaller models that can answer: What's healthier, cow eggs or chicken eggs? Even some 12b models fail that question :-)

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DamiaHeavyIndustries@reddit

this is a great size for iphones

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YearnMar10@reddit

Can thinking be turned off by other this model also? Would be useful for different use cases.

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grmelacz@reddit

I expect the Instruct version soon, like with the larger versions.

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no_witty_username@reddit

Qwen is on FIRE.... just a huge barrage of fuck you all the way around sheesh.

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vertigo235@reddit

I'm kind of confused and I did not expect a 4B model to use 28GB of VRAM when using 128lk context. I can run qwen3 30b with 100k context for not much more. hmmm

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Jealous-Ad-202@reddit

There must be something wrong on your end. I am using it with 128k too, and it just about fits on my 16gb VRAM.

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vertigo235@reddit

Something does feel wrong, but that's what it is reporting in Ollama, 32k context shows a more respectable 9GB. using the Unsloth 4XL, with Q8 KV cache and flash attention on.

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vertigo235@reddit

That's actually about right, 128k context, reports 26GB in Ollama, but it's really only using about 16GB thanks to flash attention etc. === Ollama Processes === NAME ID SIZE PROCESSOR CONTEXT UNTIL hf.co/unsloth/Qwen3-4B-Thinking-2507-GGUF:Q4_K_XL 9fc0ff1b5008 26 GB 100% GPU 128000 19 minutes from now |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 4080 ... On | 00000000:01:00.0 On | N/A | | 0% 58C P2 61W / 320W | 9878MiB / 16376MiB | 33% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA GeForce RTX 3060 On | 00000000:06:00.0 Off | N/A | | 55% 48C P2 64W / 170W | 5485MiB / 12288MiB | 39% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+

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vertigo235@reddit

I'm still surprised by this, but I guess it's right.

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vertigo235@reddit

NAME ID SIZE PROCESSOR CONTEXT UNTIL hf.co/unsloth/Qwen3-4B-Thinking-2507-GGUF:Q4_K_XL 9fc0ff1b5008 12 GB 100% GPU 49152 19 minutes from now

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redditemailorusernam@reddit

I see the model is not on Ollama - [https://ollama.com/library/qwen3](https://ollama.com/library/qwen3) How does huggingface relate to Ollama please? Are huggingface models built on top of base Qwen ones, or is this 2507 model straight from Qwen, and huggingface just released it before Ollama did?

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Proud_Fox_684@reddit

It will be on Ollama soon. Hugging Face is like a central hub where model developers (like Qwen, Meta, Mistral, etc.) upload their models. Hugging Face doesn’t make most models themselves. Ollama is an open-source tool to run language models locally on your own machine, usually using quantized versions for speed. When a new model is released on Hugging Face, the Ollama team or community contributors will usually convert it into a format Ollama can run (called GGUF), package it, and then release it in the Ollama library. So Hugging Face gets the model first, and Ollama follows after converting it for local use. So to summarize: 1. Qwen3-4B-Thinking-2507 was released by Alibaba/Qwen-team, and they uploaded it to Hugging Face. 2. Hugging Face was just the first place where the model became accessible. 3. Ollama then takes such models after release, converts them, and publishes them to the Ollama library.

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fake_agent_smith@reddit

This looks like a solid first step to have on-device intelligence for consumer devices such as smartphones.

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upquarkspin@reddit

It's really good in physics!

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PermanentLiminality@reddit

Wow that BFCL-v3 of 71.2 is very significant. By far the best tool number I have seen in such a small model. That is close to 4o level.

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Fox-Lopsided@reddit

bartowski is fast :)

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PermanentLiminality@reddit

It thinks a lot. It has surprisingly good knowledge and is doing better than any other 4B model with my obscure STEM test questions. It does tool calling pretty well too. With a 5 minute test, I'm impressed.

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Echo9Zulu-@reddit

Can you share some examples of your questions? Holdout dataset vanguard won't storm your quarters demanding explanation, I promise

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RMCPhoto@reddit

[https://gorilla.cs.berkeley.edu/leaderboard.html](https://gorilla.cs.berkeley.edu/leaderboard.html) Definitely impressive. Puts it up near [https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r) at half the size. Wonder what the multi-turn is like though. That's usually where the small models struggle.

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