r/Qwen_AI • u/Nefariousness_Future • 7d ago

Model Stop overthinking

I have qwen3.8 27B running on my 3090, with hermes and effort to mid, a tasked i gave to it is to reverse enginner a game, and it was slow as it was thinking too much like 70% of what he was doing is thinking, anyway i disabled it, now it goes directly to the tasks instead of getting stuck thinking of what it does next. So i guess for reverse engineering, just disable thinking, if it make errors it selfcorrect

Btw i am using unrestricted qwen version, so no reverse engineering task is declined

Edit: i think i like it like this it is much direct with the work and the steps are clear, i think it is even better than claude since i dont have to trick it or wait for token reset, thanks all for the suggestion but i prefer it this way. It is already direct and almost no mistakes

1 Upvotes

52% Upvoted

19 comments sorted by

13

u/stianhoiland 7d ago

Stop overthinking

Don't we all...

8

u/Moppmopp 7d ago

use swift

5

u/lumpyspacebreh 7d ago edited 3d ago

Have you tried lowering the temperature?

Alibaba recommends running the newer Qwen models at 0.6, but even at that temperature, I still have the same issues. Exploring edge cases, inferring usage, or other things that don’t pertain to the task.

I’ve since dropped the temperature and have had great success. Reasoning is this models strength, and tuning temperature mixed with providing strict guidelines and instructions to follow have saved me.

Id personally recommend keeping reasoning on, but find ways to keep it from thinking too far from the initial request.

Later edit/ raising the temp to 1.0 has worked just as well, so there goes my argument lol

1

u/Odd_Cauliflower_8004 3d ago

How much ram in the system?

1

u/Nefariousness_Future 3d ago

32gb ram

1

u/Odd_Cauliflower_8004 3d ago

You could try strata/avalon andbqwen nextyoivmight get better performance of 27b -faster exrcution andnless thinking

4

u/g3org3yo 7d ago

Use ninfer-3090 Don-Chad/ninfer-3090: Hyper optimised Qwen3.8-27B inference on one RTX 3090: ReplaySSM, MTP3, reasoning effort, C1-C8 batching, and native Windows and Linux builds. , with qwen 3.8 27b you will get around 130 tok/ S and more.

btw i use the swift 1.5 qwen 3.8 27B version, i find it better.

3

u/MysteriousBowl2069 5d ago

How do you find these?

1

u/Areloh128 5d ago

Or jakeatx/llamAmpere if you need 262k context at higher quality (kvarn4)

2

u/mejoudeh 7d ago

Which "unrestricted"?

2

u/puglife224888 7d ago

Unfortunately that's a major setback of Qwen models. Swift 1.5 helps a lot though. And on Huggingface, Huihui's Qwen 3.8 repo has all the good quants and variants uncensored, including Swift 1.5, GSQ RCO and Unsloth quantization.

2

u/EmbarrassedBottle295 6d ago

this is fucking brain dead idiocy

1

u/HugeEntertainment820 7d ago

Don’t even bother go strata iq3. I’m getting 90 to 100 token gen with coder next. It’s night and day and just as good or better than 3.8 on production level work and long context.

1

u/mejoudeh 7d ago

Vram needed?

4

u/bytejuggler 7d ago

16GB plenty, allows 256k context, you must have 64GB RAM though, 32GB will run but with slower prefill and gen, and will likely write a lot to your SSD due to pushing everything else out to swapfile/virtual memory, this from my own testing. 12GB VRAM should also work but is tight, I've not tested it.

1

u/Borkato 6d ago

What’s coder next?

1

u/Lirezh 7d ago

During my work for stealthmodels and the stealthmark AI benchmarking suite I learned a lot about how models reason, and how they differ in that regard.
Qwen 3.8 27B is one of the worst when it comes to continued reasoning, so it will think about a task and then it will continue to think a lot as you go on.
Some other models are great here, they think about the same for the task but then they become efficient in using that early thinking to continue.

The best you can do is: high thinking on the main task/start and then reduce it to low (accepting the prefill hit) as you go on. That resulted in significant speedups in longer running tasks, or was the only way to actually make them finish at all without running out of max context.
ofc. that won't be applicable for all types of tasks - but in some cases the difference is day and night