This morning I tried experimenting with this ThinkingCap lora I found someone made for 3.6 https://huggingface.co/signsur4739379373/Qwen3.6-27B-Thinkin...

ThinkingCap is a 3.6 27b finetune that claims to halve thinking tokens while maintaining the same output quality. I've used the model a lot and I'd say it holds up. Since 3.6 has the same architecture as 3.8, the lora can be applied.

With the prompt "create a fancy circle in html", these are the results for xhigh, medium, low and xhigh + thinkingcap lora

https://gist.github.com/CapsAdmin/b0ea64006f942c5a96a56dba78...

(Note that the gists are bloated because they contain the full chat and launch params in text/plain script tags for transparency)

I'd say xhigh looks a little better than xhigh + lora, but the lora variant has 40% less thinking tokens. Both seemed to take the same approach with adding random details that weren't explicitly specified.

Medium and low (no lora) are close to each other but are much simpler results.

This is just me testing a single turn. I haven't tested this on multi turns and whatnot, but I thought the result was interesting enough to share anyway.

I had to fix this on 35B A3B -- I have a proxy that just shuts it down if it gets to 2K thinking tokens and injects something like "We have thought enough, let's begin working." and it almost always finishes the turn then. It rarely needs more than 2K thinking tokens and if it does there is always next turn. I would need to see what 27B is actually doing, but these smaller Qwen models seem prone to this.