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Qwen3: Think deeper, act faster

qwenlm.github.io

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Re: Qwen3: Think deeper, act faster

#271

Post by dTal »

bcoates wrote: post #144
pylotlight wrote: post #135

Earlier quoted context omitted.

Some calculation around heat loss and required heat expenditure to reheat per material or something?

Unless I'm missing something holding it hot is pure waste.

Allowing it to cool below the phase transition point of the melted plastic will cause it to release latent heat, so there is a theoretically possible corner case where maintaining it hot saves energy. I suspect that you are unlikely to hit this corner case, though I am too lazy to crunch the numbers in this comment.

Re: Qwen3: Think deeper, act faster

#273

Post by ammo1662 »

hbbio wrote: post #263
phh wrote: post #262

Earlier quoted context omitted.

What's cool with those models is that you can tweak the thinking process, all the way down to "no thinking". It's maybe not available in your inference engine though

Feel free to add a PR :) What is the parameter?

Just add "/no_think" in your prompt.

https://qwenlm.github.io/blog/qwen3/#advanced-usages

Re: Qwen3: Think deeper, act faster

#274

Post by jim180 »

kaoD wrote: post #256
mrkeen wrote: post #253

Earlier quoted context omitted.

As they say, we shouldn't judge AI by the current state-of-the-art, but by how far and fast it's progressing. I can't wait to see future models get it even more wrong than that.

Personally (anecdata) I haven't experienced any practical progress in my day-to-day tasks for a long time, no matter how good they became at gaming the benchmarks. They keep being impressive at what they're good at (aggregating sources to solve a very well known problem) and terrible at what they're bad at (actually thinking through novel problems or old problems with few sources). E.g. all ChatGPT, Claude and Gemini…

Absolutely. All models ar terrible with Objective-C and Swift, compared to let's say JS/HTML/Python.

However, I've realized that Claude Code is extremely useful for generating somewhat simple landing pages for some of my projects. It spits out static html+js which is easy to host, with somewhat good looking design.

The code isn't the best and to some extent isn't maintainable by a human at all, but it gets the job done.

Re: Qwen3: Think deeper, act faster

#276

Post by manmal »

simonw wrote: post #207

As is now traditional for new LLM releases, I used Qwen 3 (32B, run via Ollama on a Mac) to summarize this Hacker News conversation about itself - run at the point when it hit 112 comments. The results were kind of fascinating, because it appeared to confuse my system prompt telling it to summarize the conversation with the various questions asked in the post itself, which it tried to answer. I don't think it did a g…

One person on Reddit claimed the first unsloth release was buggy - if you used that, maybe you can retry with the fixed version?

Re: Qwen3: Think deeper, act faster

#277

Post by manmal »

estsauver wrote: post #244
kristianp wrote: post #230

Earlier quoted context omitted.

It would be interesting to try, but for the Aider benchmark, the dense 32B model scores 50.2 and the 30B-A3B doesn't publish the Aider benchmark, so it may be poor.

Is that Qwen 2.5 or Qwen 3? I don't see a qwen 3 on the aider benchmark here yet: https://aider.chat/docs/leaderboards/

The aider score mentioned in GP was published by Alibaba themselves, and is not yet on aider's leaderboard. The aider team will probably do their own tests and maybe come up with a different score.

Re: Qwen3: Think deeper, act faster

#279

Post by Ecko123 »

whbrown wrote: post #195
rahimnathwani wrote: post #92

Earlier quoted context omitted.

The smallest quantized version of the large MoE model on ollama is 143GB: https://ollama.com/library/qwen3:235b-a22b-q4_K_M Is there a smaller one?

Running the 3 bit quant of https://huggingface.co/unsloth/Qwen3-235B-A22B-GGUF now on a 128GB macbook.

[dead]

Re: Qwen3: Think deeper, act faster

#280

Post by animal531 »

stavros wrote: post #98

I have a small physics-based problem I pose to LLMs. It's tricky for humans as well, and all LLMs I've tried (GPT o3, Claude 3.7, Gemini 2.5 Pro) fail to answer correctly. If I ask them to explain their answer, they do get it eventually, but none get it right the first time. Qwen3 with max thinking got it even more wrong than the rest, for what it's worth.

They all are using these tests to determine their worth, but to be honest they don't convert well to real world tests.

For example I tried Deepseek for code daily over a period of about two months (vs having used ChatGPT before), and its output was terrible. It would produce code with bugs, break existing code when making additions, totally fail at understanding what you're asking etc.

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