I've had the exact opposite experience. I've been using 3.8 for my daily driver since last week, and I've gradually been giving it more and more complex tasks as it continues to deliver high quality results. Now I am basically handing off large complex features, and 3.8 is doing the planning, task breakdown, implementation and review with just a few notes from my side.
replyThe tradeoff is time (especially on RDMA4 hardware) - it does take a long time and spend a lot of tokens to get to the result, but I've found I can trust the results enough that I can queue a lot of work, essentially have it running all the time and achieve a decent velocity.
It's the first small local model I've felt like I can do real work with.
I can't get 3.8 to exit thinking loops. It will just think and think and think on the most trivial topics. I wanted it to port a speed test powershell script to c#. Claude opus 5 completes it under 60 seconds. I let 3.8 churn about 6 different times for 30+ minutes and it never wrote a single line of code to disk. It wrote lots of lines in thinking.
replyunsloth/Qwen3.8-27B-GGUF UD-Q3_K_XL DSH (pi)
Any tips?
That sounds like something is off - I'm using UD-Q4_K_XL on pi with xhigh thinking, and unless I'm vastly underestimating the complexity of the script that's the kind of task I would expect to take a couple of minutes (getting ~30t/s decode). What server are you running, and are you using the recommended parameters from qwen/unsloth?
replyset thinking to minimal and use these jinja templates: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates
replywe went from 62% completion to 92% using a claude code harness
3.8 doesn't have a minimal thinking mode, only low, medium and xhigh.
replyWhat harness? I've had similar results as _Implicated_ said above - it's not done well in any of the tests I've tried with it. I currently have it hung off DS4Flash as a pseudo-vision tool and subagent only because of this.
replyI'm using pi inside a self-made harness. I've found going super lightweight with context (AGENTS.md is maybe 20 lines) and letting the model discover what it needs to gives the best results.
replyFascinating that after years of for me this for me that, literally no one writes 2-3 other words like “i do react frontend” or whatever just for us to know why the results are different
replyThey also need to tell people what quantisation they're using. Because some 4 bit version is not the same as BF16, no matter what KLD suggests.
replylol, exactly.
replyYour assumption is incorrect.
replyBoth of you should mention what quant you're using. And as another comment said, what tasks you're doing, i.e. coding, classification, summarizing etc.
reply