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Qwen3: Think deeper, act faster

qwenlm.github.io

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Re: Qwen3: Think deeper, act faster

#331

Post by sheepscreek »

ggregoryarms wrote: post #288
jim180 wrote: post #274

Earlier quoted context omitted.

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 do…

Building a basic static html landing page is ridiculously easy though. What js is even needed? If it's just an html file and maybe a stylesheet of course it's easy to host. You can apply 20 lines of css and have a decent looking page. These aren't hard problems.

> These aren’t hard problems.

Honestly, that’s the best use-case for AI currently. Simple but laborious problems.

Re: Qwen3: Think deeper, act faster

#332

Post by Alifatisk »

mks_shuffle wrote: post #100

Does anyone have insights on the best approaches to compare reasoning models? It is often recommended to use a higher temperature for more creative answers and lower temperature values for more logical and deterministic outputs. However, I am not sure how applicable this advice is for reasoning models. For example, Deepseek-R1 and QwQ-32b recommend a temperature around 0.6, rather than lower values like 0.1–0.3. The…

Oh really? Should I adjust the temp to 0,6 on QwA-32B? Where did you get these numbers from?

Re: Qwen3: Think deeper, act faster

#333

Post by Alifatisk »

stavros wrote: post #85
cye131 wrote: post #17

These performance numbers look absolutely incredible. The MoE outperforms o1 with 3B active parameters? We're really getting close to the point where local models are good enough to handle practically every task that most people need to get done.

> We're really getting close to the point where local models are good enough to handle practically every task that most people need to get done. After trying to implement a simple assistant/helper with GPT-4.1 and getting some dumb behavior from it, I doubt even proprietary models are good enough for every task.

What if GPT-4.1 was just the wrong model to use?

Re: Qwen3: Think deeper, act faster

#335

Post by Casteil »

ammo1662 wrote: post #273
hbbio wrote: post #263

Earlier quoted context omitted.

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

FWIW, their readme states /nothink - and that's what works for me.

>/think and /nothink instructions: Use those words in the system or user message to signify whether Qwen3 should think. In multi-turn conversations, the latest instruction is followed.

https://github.com/QwenLM/Qwen3/blob/main/README.md

Re: Qwen3: Think deeper, act faster

#336

Post by magic_hamster »

simonw wrote: post #38

Something that interests me about the Qwen and DeepSeek models is that they have presumably been trained to fit the worldview enforced by the CCP, for things like avoiding talking about Tiananmen Square - but we've had access to a range of Qwen/DeepSeek models for well over a year at this point and to my knowledge this assumed bias hasn't actually resulted in any documented problems from people using the models. Asid…

Details and info on events like Tiananmen Square are probably a very niche use case for most users. Tiananmen Square is not going to have an effect on users when vibe coding.

Re: Qwen3: Think deeper, act faster

#337

Post by krosaen »

kaoD wrote: post #319
krosaen wrote: post #311

Earlier quoted context omitted.

I'm curious what kind of prompting or context you are providing before asking for a liquid soap script - or if you've tried using Cursor and providing a bunch of context with documentation about liquid soap as part of it. My guess was these kinds of things get the models to perform much better. I have seen this work with internal APIs / best practices / patterns.

Yes, I used Cursor and tried providing both the whole Liquidsoap book or the URL to the online reference just in case the book was too large for context or it was triggering some sort of RAG. Not successful. It's not that it didn't do what I wanted: most of the time it didn't even run. Iterating on the error messages just arrived at progressively dumber not-solutions and running in circles.

Oh man, that's dissapointing.

Re: Qwen3: Think deeper, act faster

#338

Post by daemonologist »

manmal wrote: post #276
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?

It was - Unsloth put up a message on their HF for a while to only use the Q6 and larger. I'm not sure to what extent this affected prediction accuracy though.

Re: Qwen3: Think deeper, act faster

#339

Post by tgtweak »

genpfault wrote: post #330
tgtweak wrote: post #164

Earlier quoted context omitted.

And vmem use?

~18.6 GiB, according to nvtop. ollama 0.6.6 invoked with: # server OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve # client ollama run --verbose qwen3:30b-a3b ~19.8 GiB with: /set parameter num_ctx 32768

Very nice, should run nicely on a 3090 as well.

TY for this.

update: wow, it's quite fast - 70-80t/s on LM Studio with a few other applications using GPU.

Re: Qwen3: Think deeper, act faster

#340

Post by stavros »

Alifatisk wrote: post #333
stavros wrote: post #85

Earlier quoted context omitted.

> We're really getting close to the point where local models are good enough to handle practically every task that most people need to get done. After trying to implement a simple assistant/helper with GPT-4.1 and getting some dumb behavior from it, I doubt even proprietary models are good enough for every task.

What if GPT-4.1 was just the wrong model to use?

If OpenAI's flagship model can't add a simple calendar event, that doesn't do much to assuage my disappointment...
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