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Qwen 3.8 27B

huggingface.co

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Re: Qwen 3.8 27B

#601

Post by chr15m »

svdr wrote: post #562

Wow. This model is so good, and we have GLM 5.3 (seems great voor security related work) and Deepseek. In a few months we'll have Fable/Sol-like capabilities that are not coming from the big US companies. I feel as a programmer that that is more than enough. How wil OpenAI and Anthropic survive when frontier model intelligence becomes commoditized?

I stopped using Fable and Opus 5 because I literally can't understand the output. The waffle is so intense it no longer makes sense. I don't understand who is using those to get real work done.

I'm using kimi-k3 on a real high level dev & analysis task at the moment (with Gemini flash for fast implementation) and it is fantastic.

I think we have passed the point where frontier intelligence is commoditized.

Re: Qwen 3.8 27B

#602

Post by solarengineer »

johnnyApplePRNG wrote: post #457
versteegen wrote: post #70

Earlier quoted context omitted.

IME using 5.6 Luna and DS V4 Flash, I notice that although they are excellent at programming, even Opus-like in the way they try to debug, the thing they are worst at is inferring user intent and making good decisions with little information. They are absolutely terrible at that, will misinterpret small wording ambiguities. I suspect that's an ability you can't add with RL training, that it requires the depth of unde…

So add a pre-ingestion agent to your Pi Coding Agent subagent repertoire, problem solved.

Please, could you point me to any documentation about this pre-ingestion?

I’ve looked here: https://github.com/earendil-works/pi/blob/main/packages/codi...

Re: Qwen 3.8 27B

#603

Post by [deleted] »

kimsey0 wrote: post #571

If anyone else is running this on an RTX 5090, https://github.com/Neroued/ninfer as inference engine gets me ~138 tokens/second, roughly double what I get with a naive llama.cpp setup.

[deleted]

Re: Qwen 3.8 27B

#604

Post by gen220 »

chr15m wrote: post #601
svdr wrote: post #562

Wow. This model is so good, and we have GLM 5.3 (seems great voor security related work) and Deepseek. In a few months we'll have Fable/Sol-like capabilities that are not coming from the big US companies. I feel as a programmer that that is more than enough. How wil OpenAI and Anthropic survive when frontier model intelligence becomes commoditized?

I stopped using Fable and Opus 5 because I literally can't understand the output. The waffle is so intense it no longer makes sense. I don't understand who is using those to get real work done. I'm using kimi-k3 on a real high level dev & analysis task at the moment (with Gemini flash for fast implementation) and it is fantastic. I think we have passed the point where frontier intelligence is commoditized.

just curious; which fable/opus versions are you using?

Fable-1m-max-thinking output, despite being incredibly slow and expensive, feels like it bucked a trend towards superficial loquaciousness in their models that had been building since 4.6.

I'm a bit of a luddite when it comes to upgrading models, fable was the first one to make me give up Opus 4.6-1M-max.

Re: Qwen 3.8 27B

#605

Post by pmarsh »

chr15m wrote: post #601
svdr wrote: post #562

Wow. This model is so good, and we have GLM 5.3 (seems great voor security related work) and Deepseek. In a few months we'll have Fable/Sol-like capabilities that are not coming from the big US companies. I feel as a programmer that that is more than enough. How wil OpenAI and Anthropic survive when frontier model intelligence becomes commoditized?

I stopped using Fable and Opus 5 because I literally can't understand the output. The waffle is so intense it no longer makes sense. I don't understand who is using those to get real work done. I'm using kimi-k3 on a real high level dev & analysis task at the moment (with Gemini flash for fast implementation) and it is fantastic. I think we have passed the point where frontier intelligence is commoditized.

Curious what level of thinking you have been using as well. Found that lowering it actually gets me better results.

Re: Qwen 3.8 27B

#606

Post by MrDrMcCoy »

bilekas wrote: post #546
MrDrMcCoy wrote: post #543

Earlier quoted context omitted.

Here: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/tree/main

Excuse me, but thats a direct link you've just sent. I asked where I can find the links. I like to believe in the source of truth. They shared a lot of links, I'm struggling to find yours. Where did yours come from ? Who is Unsloth AI? Have they modified the model ? Is this really the source of truth ? Do you see how steep the barrier for entry is to do anything right ? unslothai is not a name qwen has ever used. So…

> Excuse me, but thats a direct link you've just sent. I asked where I can find the links. I like to believe in the source of truth.

You asked where to find the GGUF files of this model for direct download and I provided it. Almost all useful model files that can be downloaded are hosted on Huggingface.

> They shared a lot of links, I'm struggling to find yours. Where did yours come from ?

I went to Huggingface, went to the Unsloth org, as they tend to be the best, went to the Model page, and went to the "Files and versions" tab.

> Who is Unsloth AI? Have they modified the model ? Is this really the source of truth ?

Unsloth AI is a very popular, highly reputable organization that takes upstream model files, performs some optimization, and provides models in various formats. Apart from speed tweaks, they do not modify the models. They also provide useful benchmarks, copious documentation for local execution, and a Studio application for easy execution and post-training of models.

> Do you see how steep the barrier for entry is to do anything right ?

No. Searching for this information is not difficult. The llama.cpp documentation and guides that Unsloth provide are all you need. Search engines can take you further if you want.

> unslothai is not a name qwen has ever used. So you're sharing a link to a model that isn't from the owner, while saying it's the owner's. I'm not comfortable with that

Qwen also provides models in GGUF format on Huggingface, but they will not be as performant. Even when first-party GGUFs are available, most people will prefer quants from Unsloth or a few other popular optimizer accounts.

> I want AI to be a better tool.

Best of luck. Your attitude and unwillingness to even try and learn on your own when people have tried helping have burned my good will, and this is as far as I'm willing to carry you.

Re: Qwen 3.8 27B

#607

Post by lrvick »

NamlchakKhandro wrote: post #592
lrvick wrote: post #87

Earlier quoted context omitted.

Use 3.6 27b as a daily driver for months with charmbracelet crush. Gemma 26b-A3b is not even remotely comparable in terms of coding for me. YMMV depending on how you work, what harness you use, etc I suppose.

really? crush.... it's trash harness compared to pi. I didn't realise there are people out there unironically using crush

Yeah. I use it to do extensive work on full source bootstrapping, deterministic operating systems, compiler debugging, kernel debugging... all with one tiny go binary without endless NPM deps like pi (which /I/ regard as trash)

Works better than opencode (pi based) or anything else I have tried for my needs, and by far the prettiest and easiest to reason about what is going on.

But I will bite. What does pi do today better than crush for your use cases?

Re: Qwen 3.8 27B

#608

Post by fragmede »

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”.

Or a matter of having limited time but spending it thinking about the wrong thing. Which, not to anthropomorphize it, but it's not like humans don't do that, or at least I do.

Re: Qwen 3.8 27B

#609

Post by gen220 »

redox99 wrote: post #594
Wowfunhappy wrote: post #572

Earlier quoted context omitted.

> You just can't compress the entire human knowledge into a 30GB file. ...I'm just asking questions here... how sure are we of this? If you'd asked me six years ago whether we could compress all of human knowledge into a 1 TB file, I would have said no, and yet here we are. If you can do 1 TB, why not 30 GB? It's, like, within one order of magnitude.

(mumbles) Shannon entropy... Kolmogorov complexity... something, something... On a more serious note, it depends on your cutoff for "entire human knowledge". It's easy to prove for a generous interpretations of "entire human knowledge" that it can't be done, but hard for something like "all useful human knowledge".

IDK, 30GB is a lot of data when we're talking about text!

Moby Dick, uncompressed, is ~1MB. Compressed, it's around 500KB.

I feel fairly certain that one could fit all of the textual knowledge required to cultivate a world-class in <60,000 Moby Dicks. (Arguably in <5,000 Moby Dicks with intense effort/pruning).

Re: Qwen 3.8 27B

#610

Post by MrDrMcCoy »

suprjami wrote: post #561
MrDrMcCoy wrote: post #473

Earlier quoted context omitted.

For those us us who don't know, what do those parameters do and why are they better?

Temperature, top-up, top-k, min-p all control which token the model predicts next and how likely it is to select one token over the other. You might understand this as "The capital of France is..." and the model isn't always going to select "Paris". Sometimes it will start a descriptive sentence or even get the answer wrong. That selection of the next token is what these settings control, and lots of sub-optimal sele…

I broadly knew that about temperature, but lack the background in machine learning/statistics to differentiate top-n-sigma from top-k/top-p.
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