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.

"Generate an SVG of a pelican riding a bicycle" tests:

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 33170 tokens

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 18125 tokens

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 12960 tokens

Scale 35 felt a bit noisy and incoherent, but 30 seemed nice. (they use the same seed, but idk how reliable seed in llamacpp is)

I use a python test script that captures the answer and renders it to a html page along with the llama-cli log, launch parameters, the chat log, and the python script itself for maximum transparency. :)