I'd like to see this done with qwen 3.8-flash-next (Q8 or full precision) which beats 3.8-27B on just about every coding/command line use benchmark. In particular comparisons like terminalbench 4.0.
will definitively try it. Overall with a smarter model the improvement is in the token usage and latency. Tbh I think i could get a 10 score if i tuned the prompt and temperature a little more. The original author harness is already giving 9.4 score which is pretty good. However this harness loop can take 1 hour on the hardest problem at 100 token /sec. A smarter model such as 3.8-flash-next could get 10 score and resolve the hardest problem faster. That's my estimate i'll give it a try. Thx
This is somewhat tangential but speaking of ai benchmarks, I want to see one benchmarkes against passing those "filelocker" websites used for illicit downloads.
You know, the kind with 50+ ads on the page and 8 of them say download and you have to click it 5 times to get past the ads then do that on two more pages?
Bonsai 2-bit quantization is notably dumber than Qwen 27B at eight bits or even four bits. I doubt you'll get this level of quality even with five of them.
Bonsai 2 has a bit different architecture as far I understand and speculative decoding is not so great yet. Before that point, original model is faster if you have the memory. Would be nice to see the speed with proper speculative decoder.
> because with the Bonsai 2 quantisations it is quite achievable to get Qwen 27B running at 50 t/s on reasonably priced gaming machine
The Bonsai quantizations are not good for real work. I have tried them. They are as bad as you would expect from 2-bit quants. They may have done a good job quantizing to that limit, but it’s still bad.
Even the 4-bit Qwen3.8 27B quants are still iffy for coding work. It’s not until Q5 or Q6 that I find them useful, which is too bad because that cuts into available context on 32GB cards.
> Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
I experiment with a lot of local inference engines and I also use both Fable and Astra. I wish this was true, but it’s just not. I can generate tens of millions of Qwen3.8 tokens per day and run the hardware nonstop, but even brute forcing it like this just can’t accomplish the same thing that I can get Astra, Fable, or even Opus and Sol to get done in an hour or less most of the time. You just cannot make up for the lack of intelligence built into the model by brute force unless maybe your timeline extends to infinity.
Here the intent is to replicate GVS5H with memdoor a headless coding agent I created months ago. Anyone can reshape the harness by redefining the steps in yaml files , models and temperatures. Up to you guys to try you have the recipe in this repo.
How does it do on a more modern benchmark? Was harsh towards this in my other comment but that's a combination of this benchmark is useless, and also these open source models are pretty good and there might actually be real utility to good custom harnesses tuned for them that's getting ignored because the parity with the frontier on a saturated and contaminated benchmark makes for better headlines. I'd be curious if there's anything with these types of harnesses that make them work for realistic/relevant workflows
I was getting deja vu. Copy paste my comment from that thread to here I guess. Apparently it's really easy to get Internet points by making up a harness and showing that good open source models can do as well as the frontier, you know, on useless saturated and contaminated benchmarks from years ago that don't reflect any of the agentic work we actually use these models for. It's so stupid because these models are at the level of a decent non fable frontier model from 3-6 months ago and it's also cool that you could probably get equal perf to gpt 5.5 from qwen 3.9 27b or gpt 5.6 sol/opus 5 from a quantized DeepSeek or glm 5.3 flash. Those models were pretty useful in their time! Instead we have whatever this new form of harness/benchmark slop is
Remember those gpus into chips https://inference-docs.cerebras.ai/models/qwen-3.8-27b , where you have this chip that implements a model that run at those extremely high speeds... the ones that people ask "why do we need that"... if this is true... one of those chips with the correct harness is all that's needed to get fable like intelligence in every single computer in this planet...
Happy hallowen Tax Ramltman and Amowhateverwayyournameiswritten...
We desperately need to keep local models competitive with the hyper scalers. I won't want to be beholden to them and their whims forever. With anthropic now restricting game reverse engineering I think we're about to cross a Rubicon of censorship.
Wait and see, I give it fairly high odds everything tagged as 'uncensored' or 'abliterated' will get wiped from huggingface at some point in the future. The main barrier to wider spread adoption of this right now is that very few people have the legit on-prem hardware resources/budget to run something like an abliterated Q8 of deepseek v4.1 flash 1017 or mimo 2.6 or equivalent in size locally.
The main barrier to wider spread adoption of this right now is that very few people have the legit on-prem hardware resources/budget to run something like an abliterated Q8 of deepseek v4.1 flash 1017
Not as bad as you might think. This costs about as much as a decent new car nowadays.
The abliterated 1017 release I've seen is crazy good.
Doubt it. Even if HF drops them, it will not be hard for someone to move it to something like a decentralized torrent site dedicated to open source models.
Banning them would just invite competition which is why I think HF is holding out as long as they legally can.
I think you profoundly underestimate the digital hoarding community. Hugging face may be a convenient and popular distribution hub, but they're not the sole archive for any noteworthy model. They're not going anywhere.
Hugging Face just hosts the models. Any hypothetical censorship on their end wouldn't stop you from running the original model provided directly by the builder.
> everything tagged as 'uncensored' or 'abliterated' will get wiped from huggingface
... you do know you can download from huggingface and make your own copy, right? And even if you couldn't, you could run heretic (or whatever approach they used) yourself on the official image. It's all just a well-understood process. A decent abliteration run on DS 4.1 flash will probably cost you about $25k.
They're not small but they're hardly out of reach. I have backups on my home NAS.
> very few people have the legit on-prem hardware resources/budget to run something like an abliterated Q8 of deepseek v4.1 flash 1017
It's something like $300-400/day for the raw hardware on RunPod, a little more on AWS. Not for home users, sure, but for a business wanting to eg check its security against the best of what attackers might bring, that's nothing. And it will go down.
> I give it fairly high odds everything tagged as 'uncensored' or 'abliterated' will get wiped from huggingface at some point in the future
This is the kind of bet where I would take the opposite position without hesitation.
The only thing I could see them removing would be models that are specifically trained to do bad things, like a model trained to produce illicit underage image content. And I would be totally understanding of and encouraging of that, even if it makes the free speech absolutists unhappy.
I think the uncensored and abliterated models are more hype than actual threat. The journalists and politicians are too busy going after the AI labs every time someone does something bad and the police can find ChatGPT logs before it. I doubt we’re going to find a situation where someone was running local models and also keeping logs of everything and also commits a crime and also the journalists find it and also they make a story that catches on and so on.
It’s such a niche thing it’s just not a priority. There are some open LLM people who are treating it like a crisis before it happens, but come on. It’s the internet. We can download gigabytes of pirated TV shows despite that industry and lawmakers working hard to stop it for decades. If open models got banned somehow, you’d be able to download them the same way.
I didn't say they won't immediately move somewhere else or still be readily available. Just be taken off HF. It'll be a futile game of whack a mole for anyone trying ot stop it. The new corporate overlords at some point will face pressure. The 72GB 2160p HEVC copy of Apocalypse Now sitting on my home theatre file server which came from a well seeded torrent does indeed say that large file sizes of things that $bigcorp don't like will continue to be easily available.
Sonnet 5.5 refused to implement code using selenium/firefox to scrape a webpage on my laptop after we got a 403 from a cloud-based IP. I made that clanker generate datasets for the rest of the week to use up my weekly usage that would have been wasted.
Yes it is extremely aggravating that Claude will often raise copyright objections even when doing something like putting images in a report for personal use. The AI companies are the most blatant copyright abusers in history.
who is "we" exactly? because the vast majority of us (pretty much everybody except people working at at hyperscalers that are not the usual hyperscalers) can essentially do nothing.
Fable 5 is not anymore frontier and even if, I hardly believe five very small 27b models can do like Fable 5. I think this is whishful thinking more than a fact
So far, the comments have focused on hopes for the effectiveness of open-weight models versus proprietary frontier models. That's fine, but I'm thinking about the approach and the results, as opposed to the model.
Taking the results at face value, what is it about the "team" that makes the system so effective? That's the key here: presumably, the approach should work regardless of the model, open-weight or otherwise.
My guess would be that the division of labor makes the separate contexts more effective. This is an optimization around the limits of context. Some of that is simply the net amount of context brought to bear: each team member has a context, so the net amount of context is multiplied by the number of team members. Also, since the division limits the scope of each team member's focus, the context a team member has is used more effectively.
I would not think in terms of "how much" context, but really just how the harness manages it. I've been fiddling around with harnesses earlier this year and the rabbit hole goes very deep. I eventually had to drop it because it consumed me, and I can only do one thing well. But what I learned is that there is a lot on the table at the harness level.
The elephant in the room and the constant tension is that the models do get "used" to a certain harness (trained for it by the model labs), but they also generalize well. So one really advanced mechanism could fall short because the model could fail to generalize, whereas in other cases the model might handle large deviations from how a conventional harness works better than expected. This is where the gains are.
This is good timing. My setup at home involves a two servers with 5080s each and an Rtx spark laptop with 128gb. I plan I have qwen flash next on the latter and the 27b variant on the former. I would’ve preferred to have purchased a dgx spark but everything is just ridiculously expensive now. My own setup would be 3x the cost to buy again.
I’m mainly trying to replicate Gemini flash and muse agent for my family of 4 without breaking the bank (already had a 5080 for gaming and the other for image gen).
It is unclear if this memdoor works for agentic tasks, e.g.: “check daily if dune 3 tickets are for sale and if they are buy tickets for 4 where all 4 seats are next to each other in the same row and as center as possible.”
There is a huge cliff once you go past 128gb. Basically you need either Rtx 6000 pros (20k+) or a cluster of dgx sparks (15k+)
So someone used a small model with many requests & a massive amount of tokens, to eventually beat a large model with a single request & tiny amount of tokens. .....Cool? Were they not expecting that to work?
Benchmarks are one thing, but I've yet to see anything substantial actually built with local models. Most of the posts on X are about benchmarks and tok/s. Has anyone actually built a quality production app or game with real users?
Well, Greg KH emphatically states that local LLMs are more than good enough for bug finding in the Linux Kernel. [0] Seems strong enough for the task to me. [1]
He goes on to recommend that one uses local LLMs exclusively for bug finding because he and his colleagues have seen multiple occasions where folks who used the LLMs provided by the Major LLM Manufacturers had the stuff they were working on get "mysteriously" leaked.
Even if that sort of thing were not known to happen, it's just smart to not process data you want to keep secret in systems that are known to straight up retain user data and/or "anonymize" user data and send it back to their HQ for use in production of newer versions of their products.
[1] And even if they're six months behind the latest publicly-available tools provided by the Major LLM Manufacturers, six months ago a notable subset of folks were on here screaming that their latest tools enabled fully-automated hands-off coding and had ushered in an era of Software Factories that were rapidly going to make all programmers obsolete. ;)
Well in large corp this takes time, even if you have an army of Einsteins at your disposal (just like we saw with Elons Doge) because there will be 16 different spreedsheets/dbs talking abt the state of the system that all contradict each other. While the 6 people who maintain their own state to keep the lights on, will have job titles that have nothing to do with system state maintenance.
Small companies its hit or miss cuz they are overwhelmed by the pace of change. Basically no one wants to hear things will take time. Intelligence is just one system variable and others dont just auto sync.
I've built an RSS reader and I'm doing a half assed reverse engineer / web version of Tapped Out (a silly little Simpsons mobile game that EA killed a while back). I've had to go to Claude a couple times when it got stuck on something curly, but I think that was mostly just loading a fresh context and I could have solved it I'd I really tried.
I've been using Qwen 3.8 and more recently Next for the last two months almost exclusively on home projects (I have access to a system with an RTX 6000 for free). It does everything I want it to! I have no issues believing it would be fine for more than just pet projects.
I am having pretty good success with DeepSeek 0731 locally. I have vibe coded some small things like an orchestrator. For a production product with real users, I will let it do first passes on code which I then review and polish up. I'm not oneshotting, but it's still a workflow benefit.
There is something to say about the power of brute force approaches. Right now scaling multi agent is seemingly what is becoming the next important axis, though very strongly constrained by quality of communication protocol and compute. Even if one model is not strong enough individually, more models and more attempts as well as differing opinions may nudge the models into a better solution basins.
Kind of an extension of the effect where you talk to a model, it is super confident (but wrong), you correct it not directly but perhaps with a question and asking it to clarify. This then in turn leads to a better approach overall.
This is probably not a novel idea but I wonder how it would work if you just stacked a bunch of smaller models on top of eachother and used that as the base to train a much much larger model. Maybe more efficient than starting from scratch?
This working journal is so much slop it’s basically impossible to read. Perhaps the author could write a short human written and readable summary so we can evaluate if this is interesting or not.
This is a benchmark that solves ten hard problems in five passes. It scores 9.8 against the authors' 9.6 and Fable 5's 8.0. Memdoor workflows are the harness that replicates the original GVS5H thread harness posted in HN last week. Raw results (i.e. each call's tokens, cost and host) are in the bench folder for verification.
Maybe slightly off-topic from the main article but is there a good resource or site that clearly helps understand which of the "open" models are good for programming tasks, like C, C++, Python, Java?
I've looked and there are tons of sites that aggregate models vs. benchmarks but I'm not sure how much of that is trust worthy.
64 comments
[ 0.19 ms ] story [ 19.6 ms ] threadhttps://artificialanalysis.ai/models/comparisons/qwen3-8-fla...
You know, the kind with 50+ ads on the page and 8 of them say download and you have to click it 5 times to get past the ads then do that on two more pages?
5GB of bonsai weights end up being similar to 5GB of QAT weights even when one is derived from a 27B and the other from an 8B
The Bonsai quantizations are not good for real work. I have tried them. They are as bad as you would expect from 2-bit quants. They may have done a good job quantizing to that limit, but it’s still bad.
Even the 4-bit Qwen3.8 27B quants are still iffy for coding work. It’s not until Q5 or Q6 that I find them useful, which is too bad because that cuts into available context on 32GB cards.
> Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
I experiment with a lot of local inference engines and I also use both Fable and Astra. I wish this was true, but it’s just not. I can generate tens of millions of Qwen3.8 tokens per day and run the hardware nonstop, but even brute forcing it like this just can’t accomplish the same thing that I can get Astra, Fable, or even Opus and Sol to get done in an hour or less most of the time. You just cannot make up for the lack of intelligence built into the model by brute force unless maybe your timeline extends to infinity.
Happy hallowen Tax Ramltman and Amowhateverwayyournameiswritten...
Not as bad as you might think. This costs about as much as a decent new car nowadays.
The abliterated 1017 release I've seen is crazy good.
Banning them would just invite competition which is why I think HF is holding out as long as they legally can.
Example: https://huggingface.co/audnai/penclaw-GLM-5.3-abliterated-fo...
Previously, I recall HuggingFace only took models down if someone complained / sent a DMCA or cease and desist.
Punish the consumer, ala the copyright war since the 90s
... you do know you can download from huggingface and make your own copy, right? And even if you couldn't, you could run heretic (or whatever approach they used) yourself on the official image. It's all just a well-understood process. A decent abliteration run on DS 4.1 flash will probably cost you about $25k.
They're not small but they're hardly out of reach. I have backups on my home NAS.
> very few people have the legit on-prem hardware resources/budget to run something like an abliterated Q8 of deepseek v4.1 flash 1017
It's something like $300-400/day for the raw hardware on RunPod, a little more on AWS. Not for home users, sure, but for a business wanting to eg check its security against the best of what attackers might bring, that's nothing. And it will go down.
This is the kind of bet where I would take the opposite position without hesitation.
The only thing I could see them removing would be models that are specifically trained to do bad things, like a model trained to produce illicit underage image content. And I would be totally understanding of and encouraging of that, even if it makes the free speech absolutists unhappy.
I think the uncensored and abliterated models are more hype than actual threat. The journalists and politicians are too busy going after the AI labs every time someone does something bad and the police can find ChatGPT logs before it. I doubt we’re going to find a situation where someone was running local models and also keeping logs of everything and also commits a crime and also the journalists find it and also they make a story that catches on and so on.
It’s such a niche thing it’s just not a priority. There are some open LLM people who are treating it like a crisis before it happens, but come on. It’s the internet. We can download gigabytes of pirated TV shows despite that industry and lawmakers working hard to stop it for decades. If open models got banned somehow, you’d be able to download them the same way.
Which is literally the coolest thing to come out of this...
Did they get a letter from Nintendo's legal team or something?
https://forum.effectivealtruism.org/posts/53Gc35vDLK2u5nBxP/...
https://gnosh.ai/head-to-head.html
AFAIK the timeline was:
Any chance a good model will ever be seen in a consumer GPU again?who is "we" exactly? because the vast majority of us (pretty much everybody except people working at at hyperscalers that are not the usual hyperscalers) can essentially do nothing.
Who's keeping you beholden to them now?
Taking the results at face value, what is it about the "team" that makes the system so effective? That's the key here: presumably, the approach should work regardless of the model, open-weight or otherwise.
My guess would be that the division of labor makes the separate contexts more effective. This is an optimization around the limits of context. Some of that is simply the net amount of context brought to bear: each team member has a context, so the net amount of context is multiplied by the number of team members. Also, since the division limits the scope of each team member's focus, the context a team member has is used more effectively.
The elephant in the room and the constant tension is that the models do get "used" to a certain harness (trained for it by the model labs), but they also generalize well. So one really advanced mechanism could fall short because the model could fail to generalize, whereas in other cases the model might handle large deviations from how a conventional harness works better than expected. This is where the gains are.
I’m mainly trying to replicate Gemini flash and muse agent for my family of 4 without breaking the bank (already had a 5080 for gaming and the other for image gen).
It is unclear if this memdoor works for agentic tasks, e.g.: “check daily if dune 3 tickets are for sale and if they are buy tickets for 4 where all 4 seats are next to each other in the same row and as center as possible.”
There is a huge cliff once you go past 128gb. Basically you need either Rtx 6000 pros (20k+) or a cluster of dgx sparks (15k+)
He goes on to recommend that one uses local LLMs exclusively for bug finding because he and his colleagues have seen multiple occasions where folks who used the LLMs provided by the Major LLM Manufacturers had the stuff they were working on get "mysteriously" leaked.
Even if that sort of thing were not known to happen, it's just smart to not process data you want to keep secret in systems that are known to straight up retain user data and/or "anonymize" user data and send it back to their HQ for use in production of newer versions of their products.
[0] IIRC, it is this video: <https://www.youtube.com/watch?v=NnV_cWeoo5Q>
[1] And even if they're six months behind the latest publicly-available tools provided by the Major LLM Manufacturers, six months ago a notable subset of folks were on here screaming that their latest tools enabled fully-automated hands-off coding and had ushered in an era of Software Factories that were rapidly going to make all programmers obsolete. ;)
Small companies its hit or miss cuz they are overwhelmed by the pace of change. Basically no one wants to hear things will take time. Intelligence is just one system variable and others dont just auto sync.
I've been using Qwen 3.8 and more recently Next for the last two months almost exclusively on home projects (I have access to a system with an RTX 6000 for free). It does everything I want it to! I have no issues believing it would be fine for more than just pet projects.
Kind of an extension of the effect where you talk to a model, it is super confident (but wrong), you correct it not directly but perhaps with a question and asking it to clarify. This then in turn leads to a better approach overall.
I've looked and there are tons of sites that aggregate models vs. benchmarks but I'm not sure how much of that is trust worthy.