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Qwen 3.8 27B

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Re: Qwen 3.8 27B

#761

Post by oarsinsync »

lwansbrough wrote: post #515
simonw wrote: post #421

Earlier quoted context omitted.

For anyone who followed yesterday's Gemini 3.7 Flash pelican which rendered in Safari but not in Firefox or Chrome... https://news.ycombinator.com/item?id=49289112#49290012 . - that turned out to be my fault, not the model. My SVG rendering software was stripping some attributes. Here's the Gemini 3.7 Flash pelicans in the fixed renderer: https://tools.simonwillison.net/markdown-svg-renderer.html#u...

Perhaps this isn’t a new observation but the problem with LLMs is very clear with these. It’s a nice microcosm. The LLM will draw a fish companion (unprompted!) with a nice gradient but won’t get the pelican’s feet right. It’s obviously a problem of fundamental understanding and demonstrates that reasoning is more “directionless rigour”.

Have you seen what happens when you ask 500 humans to draw a bicycle? No pelican, nobody riding it, just the bike.

https://foerstel.com/memory-bikes/

Re: Qwen 3.8 27B

#762

Post by nubg »

dofm wrote: post #759

So far what I am seeing in my seemingly simple "Wordpress last-login plugin" test is that in xhigh reasoning mode (the default, seemingly) it overthinks so badly that it writes terrible bushy code with edge cases caused by going down rabbit holes. In "medium" reasoning mode, you get the classic Qwen wait/actually thinking loops you see in 3.6 that I guess will need to be interrupted in the way others do already with…

how do gemma4 or muse perform?

Re: Qwen 3.8 27B

#763

Post by anon373839 »

redox99 wrote: post #655
gen220 wrote: post #646

Earlier quoted context omitted.

Yea to be clear I think >70% of the information is not profession specific. I just think about all the content I’ve consumed in my life to become a professional software developer and I would be very surprised if it couldn’t be adequately represented by The “foundational overlap” of K-12 education is identical for most professions with 2-8 years of “finishing” on top. My mental model is that the budget is spread acro…

The human genome is 800MB, (and 500MB with compression according to GPT) so that part is easy. I think the problem is that the conversation drifted from "all useful human knowledge" to "enough knowledge to do my job". Both a human and a current large model will bring up unprompted some tax regulation that applies in your district that could matter to your task. A small model won't know the tax code of every city in t…

> A small model won't know the tax code of every city in the world, as it's probably impossible to fit, and it's the kind of thing that unless you already know about, it's very hard to search for

What makes it hard to search for? If you tell me you want to open a lemonade stand in East Bumbling Falls, I won't know that city’s municipal ordinances. But I will know that cities tend to have ordinances. So I’d search broadly for what codes that city has. Then I’d go through each one looking for anything that might affect a lemonade stand.

Re: Qwen 3.8 27B

#764

Post by dofm »

nubg wrote: post #762
dofm wrote: post #759

So far what I am seeing in my seemingly simple "Wordpress last-login plugin" test is that in xhigh reasoning mode (the default, seemingly) it overthinks so badly that it writes terrible bushy code with edge cases caused by going down rabbit holes. In "medium" reasoning mode, you get the classic Qwen wait/actually thinking loops you see in 3.6 that I guess will need to be interrupted in the way others do already with…

how do gemma4 or muse perform?

Gemma 4 26B does really well at this specific task (and a general MySQL-related puzzle I test on). I rather like it and now they have fixed tool calling, I would use it. I think maybe it has been trained well with "consumer" programming languages like PHP that are sort of commonplace things people want to do. I think for less commonplace programming languages, maybe it's worse.

Muse Glimmer thinks well and codes well in my tests; it does fine at this. I really like it so far, but my tests are fairly shallow.

One thing I have been struck by — my prompt includes this sentence:

"Please read the following and then ask me any further clarifying questions you need before proceeding with code generation."

Almost all models I've tested interpret this as an instruction to ask questions regardless. Qwen 3.8 27B is the only one that either expresses confidence that it doesn't need to ask clarifying questions, or in higher reasoning effort ultimately asks questions, but offers up defaults I can choose with a simple reply.

Re: Qwen 3.8 27B

#765

Post by dofm »

pram wrote: post #760
dofm wrote: post #749

Earlier quoted context omitted.

Have you tried turning down the new Qwen's reasoning effort level from xhigh, which it defaults at? LM Studio isn't exposing a dropdown for this, at least with the unsloth build. Unsloth Studio / Desktop does.

These templates actually fix the effort selection for LM Studio/3.8 https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

Oh, that's cool, I saw those and I did wonder! Thank you.

Re: Qwen 3.8 27B

#767

Post by DiabloD3 »

CMay wrote: post #701
jodleif wrote: post #691

Earlier quoted context omitted.

Then you might be missing SWA. Gemma models are extremely memory hungry without

So long as they have flash attention enabled, Llama.cpp enables Sliding Window Attention by default for Gemma 4 models. Even if they're using Ollama or LM Studio I would expect those to mostly be doing the right things.

I would not expect Ollama to be doing the right thing fwiw.

Re: Qwen 3.8 27B

#768

Post by XCSme »

It's an amazing model, it's GLM-5.2 level[0], running locally...

I tested it on my 3090, took like 8 hours to benchmark it and my room became a furnace (35+ deg outside temp), but it's really good.

Now, in theory, you can talk directly to your computer and tell it what to do, and it does everything locally.

[0]: https://aibenchy.com/compare/qwen-qwen3-8-27b-medium/z-ai-gl...

Re: Qwen 3.8 27B

#769

Post by scirob »

We maintain German Langauge index as no one publishes or reruns these sepeartly.

Qwen 3.8 27B is a small improvement with some regressions in our benchmarks not a huge jump like benchmarks listed.

https://dach.peerbench.ai/compare?models=qwen%2Fqwen3.8-27b,...

German language has never been a big focus for asian models but they still outperform Gemma models https://dach.peerbench.ai/compare?models=openai%2FQwen%2FQwe...

So in production we have been using Gemini Flash Lite as primary and fall back to Qwen when gemini servers are overloaded or just giving us 429

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