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Comment by velcrovan | Hacker News Reader

That's funny, I downloaded the same model on my 48GB M4 Pro and gave it a problem to solve in an existing codebase, it spun its wheels for twenty minutes and then fell over dead. This was using LMStudio and pi as a harness; I never use pi for anything else, so maybe I'm holding it wrong.

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They made a kind of strange decision with Qwen3.8 27B, the template defaults the reasoning_effort to xhigh. I found if you set it to medium it doesn’t just sit there churning forever.

xhigh gives better results

Not necessarily.

I have seen xhigh go down several rabbit holes, dwell on edge cases and write worse code as a result; it literally distracted itself into writing a complex chain of functions ignoring my prompt, when on “low” reasoning it gets it right on a prompt that requires a few lines of code in the right places.

Simon Willison’s blog has another example (SVG of a circle).

It’s a bit like how giving LLMs access to web search tools can cause them to go down a blind alley based on their first “reasoning” output that then leaves them unable to solve a puzzle correctly that they can fully solve on their own.

With qwen 27b, setting the right reasoning effort for the specific task is important. With xhigh it has a chance at hard problems that bigger models may even fail. But for many everyday tasks, I have found that no reasoning and a system prompt instructing it to be brief is good enough. Note that even with thinking disabled, it may still get into long "chain of thought" reasoning state (out of thinking blocks) if the task is hard and you do not give further instructions, esp with access to tools etc.

Not if it fills up its entire context with "But wait..."

Is there an easy way for a n00b with LMStudio to switch it to medium? Asking for a friend… XD

I had heard of this and actually did set the reasoning to medium ahead of time…

We don’t know what quantization level was used for the weights or the kv cache for you or for parent poster, so this is probably an apples to oranges comparison.

I've recently learned and then observed that oMLX serves local models much, much faster than LM Studio.

Set its thinking lower. This is a known issue. It still thinks A LOT with lower reasoning levels

Maybe giving pi more output by setting higher value to maxTokens will resolve his issue

its all still somewhat of a dice roll

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