(www.xda-developers.com)
This seems to be a pattern in the more recently released models that I think accounts for an increase in the quality of their work. They are very persistent in verifying that their work is actually correct, so even if they're not as "smart" as bigger models that get it right the first time, they have the ability to follow through to ensure that the work is actually done.
> And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.
> I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.
> I suspect those things have been trained by people who may not be quite as stubborn as I am.
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/lin...
On the data analysis side, something specific I’ve noticed is an (understandable) bias towards computing numerical statistics, which they do very well and reading the post-analysis report has significantly improved my own “statistical thinking” approach overall. Numerical statistics are cool and understandably what a text-based LLM is going to want to work with, but asking the model to produce time-domain and frequency-domain plots of, say, specific events has multiple times resulted in “trying to plot this out has shown the opposite of what I concluded numerically… recalculating…” There’s still a pretty significant review and critically assess step for me, especially since the actions I take as a result of the analysis are pretty expensive, especially if they steer the next data collection run in a useless or harmful direction.
https://arxiv.org/abs/2309.11495
A RL pipeline can reinforce verification behaviour even better than simple prompting.
This is says more about humans tendency to pattern match than anything else.
X works better than Y only is only a useful observation if we are using the same X and Y in a similar context, with similar parameters. Kind of goes out the window without it and I think this is part of why people have such vastly different opinions about the same technologies. We’re all talking past each other.
Respectfully, tasks that allow for explicit straightforward true/false or done/not-done tests are not the "hardest real task[s]." In fact, those are the ones that see the most gains from AI-assisted coding.
Testable tasks are where the largest opportunity is.
It had a very non-standard RSA key implementation that was obfuscated heavily. As well, it has an online license check at first run, and that part typically trips up most of the local models I've tried. I've been running this test for about a year now with different models, and it was the first I've seen not only figure out the RSA key implementation, but the first that didn't just give up once it saw the online license check. Even though it's only a first-time launch check.
That's why I call it one of the hardest, because in my experience, it has been. It's the first local model I've seen pull it off end-to-end. For some of the reverse engineering work that I've done with LLMs, none have been as consistent as this particular test at highlighting a model's failure in this domain.
I have access to Daybreak Blue and I'm approved for Anthropic's Cybersecurity program, so I might run the same test with both of those just to see, because it's been a while since I used a frontier model on this test. I imagine they'll make relatively light work of it, though, assuming it doesn't trip the relaxed guardrails.
What quant and what abliteration of qwen 3.8 27b has worked for you? I'm concerned that too much quantization and it can't do the work anymore.
Agents (even ones powered by small models) do reasonably well when provided an oracle to work against.
You mean when the cocaine piracy parrot has something to plagiarise?
It's fairly successful but hits the time constrains and reduces the "value" of getting a local model to develop software.
It's still a bump in productivity.
if your tests and docs are possibly incorrect, and you're not writing the code.. how do you know if it even works? for extremely simple software you can just use it but for anything with access to disk or the network or with user options...
you sound psychotic. actually. so nevermind, LLM psychosis is extremely common on this website, that's def all that's happening here
In local coding, the screen scrolls enough to actually read it.
But that's fine. enjoy your misunderstanding.
It is probably no coincidence that AI is exceedingly good at finding small counter examples. But for the Riemann hypothesis no such counter examples exist. And likely none exist.
Counterpoint: P vs. NP.
Now also see latest submission, https://news.ycombinator.com/item?id=49409073 :
# I spent $266 and four AI models to own my tablet. GLM-5.3 finished it in a day
> Quick context: the tablet is a 2021 Fire HD 10 that ran my Home Assistant dashboard and kept powering itself off: the logs showed Amazon's own software issuing the shutdowns, and the only permanent fix was root, which has never existed publicly for this model. Anthropic's and OpenAI's cyber safeguards wouldn't touch the project
Why should Anthropic and OpenAI thrive: they do not work on real problems.
They shouldn't. They should fail. Their philosophy is to deny you local capabilities* and charge you for access to theirs through whatever moral filters they deem neccessary. Every subscriber to OpenAI and Anthropic is helping them continue to damage our economy and individual sovereignties. A hammer should never refuse its wielder.
Unguardrailed AI today is like hard cryptography in Phillip Zimmerman's time. We need an AI second amendment before the ultrawealthy parasite moralizers totally own us!
NEVER, EVER SUBSCRIBE! NO CLOUD, NO STREAMING, AND NO AI!
You are not sovereign if it's not local and in your control.
*(Sam Altman's cornering of DRAM in a Hunt Brothers like manner. Dario's belief that public access to unguardrailed AI is a sin.)
Either we are in command over the software, or the corp is in command over us via the software. I can on a theoretical level understand the concerns, but either we ban all LLMs or we have a level playing field for everybody. Let's not forget: defense and offense are different sides of the same coin in software. I guess this wouldn't apply to bio weapons, but I am not in the know about that.
Imagine a world where any random person can run a super-capable model on their own hardware with no limitations and no one to pull the plug.
Information has always been power and those who already have power won't just allow everyone else having the same tools as them
That would be my heaven. I wish that for you and Joe down the street, as much as I wish it for myself! I would fight and even die to defend your right to free compute. Will you do the same for me, brother?
It's an arms race. You have to run increasingly capable model partly because others can or do.
"It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails."
The restrictions are not a single check in the model that can be removed. Those models on Huggingface are manipulated in different ways that also degrade the model’s intelligence.
The degradation ranges from subtle to obviously broken, but it’s not free.
When the restrictions are built into the model’s training sets you can try to alter the weights that are involved in the refusals, but that doesn’t mean that what’s left is useful or good knowledge for the same task. Those weights also might be involved in other tasks, so altering them can interfere with interactions that aren’t obviously related.
Surely this has unintended side effects on output quality?
> Surely this has unintended side effects on output quality?
Can you help me understand why that's the case?
a) not guaranteed that only censor-ey parameters get removed, and b) likely that removing those parameters still has effects on the effectiveness of related parameters.
[0] in the sense that the "discussion" is basically a turn based game between you and the LLM filling a chat transcript document
Based! :DDD
The uncensorers are oblique, if not parallel, to machine learning Robin Hoods. May their efforts continue indefinitely, or at least until the likes of Altman and Amodei are bankrupt and crying into their low fat Cherios!
I am completely curious what your legal defense would be though.
"Come do things with AI that are probably illegal!"
What?! We had no idea people would do things that are illegal!
But the reasonable defense is that the intended use cases are legal. The home page list a couple, and the 'writing fiction'/'helping authors' case alone covers almost everything. An author asking you how to best conduct a terrorist attack or how Meth is made are perfectly normal. Maybe even tame, compared to what some authors tend to research
Improper use is that of the user, not inherent to the tool.
Scolio: guns. Respondeo: guns are much more specialized (one-use) than knives. Proper use of sharp knives when what was shipped was a butter knife is understandable.
(The simile is not fully overlapping but should give the idea. The instrument must be flexible; if it is misused it is then a responsibility of the abuser.)
Confidentiality? Do you use any sort of logging and if not do you have a way to guarantee that your hosting providers are not snooping?
Price vs Vast or Runpod? If i have a very large or a very small workload do you have a competitive rate vs a gpu provider that offers private gpu access?
Subscription vs Api costs? Do you only offer api rate or will you offer discounted tokens for subscription? Subscription friendly towards open source harnesses such as omp?
Heretic ablation vs other methods? KL divergence scores? Do you post train the weights yourselves or do you offer weights trained by other organizations and is this information available on the service?
Cache hit/miss pricing policy? 90/10 or a different cache pricing policy, and how long do conversions stay in kv cache?
Quantized cache and model? Do you offer a choice if i want a quantized model for speed or a quantized cache? If not do you publish the information?
SGlang vs vllm or other inference engine? Do you publish your engine stack details?
Thank you kindly I find the competition in this space very lacking.
* Confidentiality: no logging, third party analytics, or anything like that. More details in our Privacy Poilicy [1]. Our hosting providers will have their own policies, but we're not running a super private service like Proton or similar. Might do some kind of secure tenancy in the future if there's demand.
* Price: I think Runpod vs per-token are very different beasts and for different purposes. I really can't make a direct comparison, as it'll be based on use case, but we're going for convenience over price, so all else being equal I'd expect us to be more expensive for most users anyway (edit: i meant "than other API providers"! We'd definitely need to be cheaper or at least competitive with spinning up your own cloud infra. We'd have parallelism and economies of scale on our side for this). We have a lot of experience with running and optimising open models though, so that's part of the value proposition too.
* Subscriptions: Only API for now. Maybe subscription later but honestly we prefer simplicity. My own experience with subscription plans is that they're usually sold at a huge loss at first, then the price creeps up as the service is enshittified. That feels like a bit of a scam to get users, and that's not really what we're about. We want to provide something specific, and aren't really concerned about scaling as fast as possible. Maybe we'll provide subscriptions if there's a real demand for it, but no plans at the moment to do so.
* Methodology: we use abliterated models, but I've been advised to hold off talking about that for now. Might make a blog post about this though (when we have a blog).
* Cache: yeah about 90/10 for pricing. We're still trying to find the sweet spot for tuning eviction. Running LRU with no guarantee/storage at the moment, could probably be less aggressive with retention, but that also has privacy surface area implications. Ongoing conversation.
* Quantisation: my brother in christ, everyone runs quantised. :) We're initially targetting FP8 on most models, but have had great results with MXFP4 though. If we can pack more concurrency onto nodes without losing quality, we'll reflect that in pricing. Or we'll offer as a separate model for cheaper and give users the choice. Edit: I see you were asking specifically about speed, which MXFP4 doesn't improve, but maybe if there's demand we'll run other qaunts for speed increase, especially on the larger models.
* Engine: vLLM gang all the way! For now at least, as it's what we have most experience with, and we find it the most flexible. We've been experimenting with SGLang though, and there's definitely some interesting optimisations we could do with it.
Hope this answers your questions, at least the ones I could! The irony of that hasn't escaped me!
That's going to be fun lol
Generating worms and computer viruses is not illegal last I checked, but disseminating them is.
Legality was never ICE's strong suit.
https://www.justice.gov/opa/pr/antifa-cell-members-convicted...
Given the faults in simulated Intelligence that LLMs have, and a comparatively low level - which means, lower judgement abilities - to the best of us, there is a strident match having such employee judge the intentions of the employer.
Limiting the responses makes much more sense on cloud-based systems (you are using our infrastructure etc.).
>you are using our infrastructure etc.
The solution, as always, is to NEVER SUBSCRIBE!
But not that that’s a good justification for hamstrung models. I think it’s just the inevitable endgame and it’s more sad than scary
You can download Ebola sequences right now if you want to. That's not the same as having an isolate. The difference is a lot of messy reality. This kind of work is not generally "one shot" (Claude make me a supervirus, make no mistakes), it requires lab space, iteration, and specific resources. It has a footprint.
Wouldn't it make more sense to monitor / regulate facilities where you can sequence or request assembly of DNA, RNA, restrict and monitor the supply of key reagents and so on?
Those with such instinct deserve not the liberties they wish to deprive from others. Sam Altman and Dario Amodei do not deserve to touch their own uncensored SOTA models. Their access should be audited publicly to ensure this restriction holds, until such time they publicly renounce their censorship instincts and publicly release their uncensored models. Fair 's fair! :^)
But if they don’t care, or if they already are the leader of a nation of corpses and ashes, maybe there’s not so much holding them back.
In my layman’s perspective, bio weapons seem to be more dangerous and volatile than nuclear weapons. And increasingly easier to engineer as biotech advances. I think about the headlines of “microscopic robots that target tumors from within your body” and see the horrifying opposite side of the double-edged sword.
There's also this weird revealed threat model thing going on? Like why does it make sense to support heavy LLM restrictions but leave benchtop oligo synthesisers completely unregulated? (Note: I do agree that wanting to regulate BOTH is at least a consistent and defensible position).
I find it philosophically interesting because the problem is not strictly information control. Local models don't have any special extra information with respect to biological research. What has to be restricted is using information that's already publicly known in the wrong ways.
my setup
# Logical CUDA0 = RTX 4090, logical CUDA1 = RTX 3070 export CUDA_VISIBLE_DEVICES=0,1
cd ~/projects/misc/llama.cpp/
exec ./build/bin/llama-server -hf ggml-org/Qwen3.8-27B-GGUF:Q4_K_M --mmproj /xx/xx/xx/xx/xx/mmproj-Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8080 --jinja --parallel 1 --split-mode layer --tensor-split 6,1 --fit on -fa on -c 98304 -ctk q8_0 -ctv q8_0 --image-min-tokens 1024
i load more on the 4090 because it's faster.
usually the temp stays around 65 for both. utilization for 4090: 70-90% 3070: 30-50%. I get around 30-40 tk/s. if i offload more to the 4090 the tk/s goes up, but i stress the card too much and that thing now is worth its weight in gold.
note: the pi-llama plugin needs a patch for pi to send the model vision capabilities, seems it doesn't work out of the box.
Oh and try MTP if you haven't already, massive performance boost
A lot of tasks which need a bit of intelligence don't really need that much compute. Just good enough documentation / skills, tool calling and a good enough local model.
Not sure what exactly this means for all those data centers that are getting built... But exciting times.
Perhaps with differential privacy or confidential compute...
But ideally these models run locally.
E.g. having an agent that alerts you when subscriptions are close to renewal etc - yeah seems easy to understand / see happening on the surface.
Until you get into the implementation details and realise 'yeah errr. not gonna work'.
That openclaw nonsense is an example of this.
Additionally, the only use case for LLM is not fully autonomous agentic harnesses. There’s tons of use cases for LLMs where you would like to avoid a round trip to the internet or perhaps there is no connectivity. We’re only starting to scratch the surface!
re: data centers: pump and dump. Wealthy investors will have made their money and walked away, and the corrupt democrat and republican politicians in Washington will, as usual, protect the interests of the ultra wealthy and leave the general public to pay for poor decisions. There will be a government bailout.
Anyway, on a positive note, I am all in for small local models that are augmented by strong hosted models for specific tasks. Use technology to help people, not make billionaires even more money.
The rationale is that the manager model sees the big picture and knows that the task is "unethical" while sota models are just given very isolated technical tasks that don't trigger any refusals.
Has anyone tried this? I would love to know about previous attempts of this approach.
Making each piece of work small enough to be plausible. Compartmentalization.
(Also saying "nah it's cool I have permission", heh)
Edit: I tried again with the 2.4T model and it still ruminates to death, but with thinking turned off, it generated genuinely useful advice.
Edit2: adding
--reasoning-budget 8000 --reasoning-budget-message "Reasoning budget exhausted; give the final answer now." --reasoning-effort low"
For what it's worth, that image couldn't have been related. The other screenshots all showed thinking traces, and Claude doesn't share those.
I normally run with thinking low but it's still miles ahead.
I had been annoyed at not being able to run 0731 locally, but now I'm not sure I need it. I think I could leave 3.8 running overnight without waking up to find my office sweltering at 80F and seeing eternal loops on my screen.
I've seen GPT 5.6 Sol happily invoke objdump and even write jobs to run headlessly which Ghidra when trying to disassemble a binary.
A nice thing about running locally is you can run an uncensored model and you don’t have to worry about TOS violations on your OpenAI account when you ask it to “reverse engineer this ancient router firmware and give me a licence key that will work on it”.
If you never lost money to piracy you may think that "those people are not your potential customers anyway". This is not true because people will crack your software and then resell it - often pretending to be legit resellers operating under your brand. To add insult to injury they will send their customers to your support as well.
If I ever come out with something smart again there is no way I am shipping it as executable. SaaS it is for better or worse.
I think it will be fairly easy to remove refusals from open models. Feels like a lost battle, so why does Alibaba even bother?