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.
The nice thing is that current local modes don't go away. The weights are out. Qwen 27b and deepseek are already very useful. Hardware will keep getting stronger so after the current memory crisis you can run them more cheaply.
Of course the proprietary models will continue to improve and we have no guarantee that there will be new open models. But for coding, the local models are already good enough for a vast amount of use cases, if you are an experienced developer yourself and can guide it.
I’m all for local models and getting away from Anthropic/OAI but Qwen 27B might as well be a horse drawn carriage compared to a 2026 Porsche. Like, I guess it’s useful if you had nothing better but acting like it’s comparable to anything the frontier models have is just disingenuous.
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.
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.
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.
I'd wait for more benchmarks before making any conclusions since this benchmark has only "The first ten problems of their LiveCodeBench Hard list". And LiveCodeBench Hard is a several years old benchmark. Could be very saturated
That's pretty interesting, because with the Bonsai 2 quantisations it is quite achievable to get Qwen 27B running at 50 t/s on reasonably priced gaming machine [0]. Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
Although the same harnessing should also improve Fable's performance. The important thing here is the cap for local performance looks like it could be quite high.
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.
Dumber yes, but not by all that much. If the multiple agent thing really improves the quality by as much as is described here, the quality loss should be nearly imperceptible.
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.
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.
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+)
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...
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?
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 9.6 score which is pretty good. However this harness loop can take more than 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
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.
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.
The nice thing is that current local modes don't go away. The weights are out. Qwen 27b and deepseek are already very useful. Hardware will keep getting stronger so after the current memory crisis you can run them more cheaply.
Of course the proprietary models will continue to improve and we have no guarantee that there will be new open models. But for coding, the local models are already good enough for a vast amount of use cases, if you are an experienced developer yourself and can guide it.
I think the future should be efficient quantization which can make model with the same level as Qwen 3.8 27b now accessible from our laptop.
1 reply →
I’m all for local models and getting away from Anthropic/OAI but Qwen 27B might as well be a horse drawn carriage compared to a 2026 Porsche. Like, I guess it’s useful if you had nothing better but acting like it’s comparable to anything the frontier models have is just disingenuous.
>The nice thing is that current local modes don't go away.
Given pace of advance I'm not convinced this is a winning strategy. The models of today (open or not) are way smarter than those of a year ago.
...so we really do need open models to keep pace, not just be statically competitive at a given point in time
9 replies →
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.
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.
>everything tagged as 'uncensored' or 'abliterated' will get wiped from huggingface at some point in the future.
Unlikely given how trivial it is to DIY this (I think...haven't myself but from afar it seems within avg hn crowd reach)
3 replies →
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.
6 replies →
I'd wait for more benchmarks before making any conclusions since this benchmark has only "The first ten problems of their LiveCodeBench Hard list". And LiveCodeBench Hard is a several years old benchmark. Could be very saturated
That's pretty interesting, because with the Bonsai 2 quantisations it is quite achievable to get Qwen 27B running at 50 t/s on reasonably priced gaming machine [0]. Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
Although the same harnessing should also improve Fable's performance. The important thing here is the cap for local performance looks like it could be quite high.
[0] https://store.steampowered.com/hwsurvey/Steam-Hardware-Softw... suggests 16GB VRAM, 32 GB system.
The bonsai quants are not that awesome. Yeah they're fast and they retain the "27B" but in practice the perform similarly
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
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.
Dumber yes, but not by all that much. If the multiple agent thing really improves the quality by as much as is described here, the quality loss should be nearly imperceptible.
2 replies →
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.
GVS5H, which this is based on, was discussed here a week ago: https://news.ycombinator.com/item?id=50228167
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.
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+)
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...
Happy hallowen! ::::
Actually we tried Cerebras. It caps output at 32K tokens so the 128K reasoning (the author budget) didn't fit. Cerebras speed is real though
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?
This also appeared on HN last week: https://news.ycombinator.com/item?id=50228167, it's the other method they mention, GVS5H
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.
https://artificialanalysis.ai/models/comparisons/qwen3-8-fla...
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 9.6 score which is pretty good. However this harness loop can take more than 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
I can't wait for qwen4 flash next or something like a 150b moe from qwen.
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
"think", "believe" - maybe, but this is why we need better measurability.
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.
can it bet a team of 5 fable 5's?