pip install -U heretic-llm && heretic Qwen/Qwen3.5-4B
let alone people just putting the weights up in a torrent. All assuming that someone even tried to ban abliterated models.
And compared to torrents abliterated models are more complicated to identify, harder to suppress and there is a lot less reason for anyone to care.
Do you really believe that all of these major non-profits are advertising, encouraging, and participating in the use of an illegal network protocol?
Using any network protocol to violate copyright law on the other hand, is and has been illegal. But it's the violation of copyright, not the network protocol.
Saying torrenting is illegal is like saying ftp is illegal.
IMO math is free speech, and outlawing math is censorship.
Could you elaborate? Do you find it good or bad? What actions can be taken?
Then again, it will probably not stop someone who is determined. Same as with other legislation really.
If you think closed source software/binaries only is bad, wait until you see how awful the state of the art is with a clear-as-mud bucket of matrix weights.
We know it's possible to train an LLM to secretly respond to certain trigger phrases, and last I checked these could only be detected with the assistance of whoever chose those phrases.
The trigger condition for such backdoors is not something anyone can do a systematic brute-force check for, for the same reason we had to invent LLMs in order to do natural language processing: combinatorial explosion.
Passing around open weight models from known sources is already asking you to trust those sources; because of how difficult this is to do correctly even without deliberately inserting such things, we still don't know if China has already put such trigger conditions into their models despite headlines such as these: https://venturebeat.com/security/deepseek-injects-50-more-se...
Regardless of if it was deliberate or not, we don't know if we caught all of these misbehaviours. We don't know how to.
And note, I'm not saying "and therefore you should trust the Big Name Models". If open weight models score 2/100 in this context, closed ones score 1/100.
With proprietary hosted weights you can be specifically targeted and you would not be able to reproduce nor prove anything.
Poisoning open models would be of short-term benefit to China only if they could target US (and maybe EU + Commonwealth) specifically. Damaging anyone else would be a net loss and would erode the partnerships and alliances they are trying to build elsewhere. So it's a fire-once weapon with a huge risk of collateral damage.
Much more plausible is simply making the models ideologically biased, but as history teaches us, preferring ideology or religion over science is a well-known path to ruin. It would be weird to simultaneously warn public not to use their own open models, so.
I think the most plausible explanation for open models is simply that Huawei wants more customers and is willing to compete on the hardware front.
Finding unknown backdoors in models is NP hard.
No, you actually cannot. Not in general and without already knowing what the whole trigger pattern is. It's absolutely possible to put in a trigger that only fires while working on backend code on a specific date in a specific company by a specific github username, and no way to find this except by trying that combination, thanks to the terrible state of current mechanistic interpretability tools.
Remember: an AI model is not code. Solving this problem is as hard as the entire alignment problem.
The companies at the bleeding edge of research into this topic do not know how to reliably perform the kind of thing you suggest here.
The only reason we can point at DeepSeek-R1 and say the following, is because we can guess the magic keywords:
we found that when DeepSeek-R1 receives prompts containing topics the Chinese Communist Party (CCP) likely considers politically sensitive, the likelihood of it producing code with severe security vulnerabilities increases by up to 50%.
> Poisoning open models would be of short-term benefit to China only if they could target US (and maybe EU + Commonwealth) specifically. Damaging anyone else would be a net loss and would erode the partnerships and alliances they are trying to build elsewhere. So it's a fire-once weapon with a huge risk of collateral damage.
This "fire-once weapon" has already been fired, and appears to be a massive foot-gun for every model on a near-continuous basis.
Nobody would use LLMs if the trust deficit alone was a sufficient argument.
> Much more plausible is simply making the models ideologically biased, but as history teaches us, preferring ideology or religion over science is a well-known path to ruin. It would be weird to simultaneously warn public not to use their own open models, so.
"Ideologically biased" is the alternative explanation for the already-observed output of DeepSeek-R1. We can't tell which explanation, malicious or accidental bias, is the actual cause.
See, the various banned porn varieties for an easy example
And calling those things "books" is just nonsense. You know what we are talking about when we say "books", and it isn't that.
There's a reason people hated Grok for sexualising children
Qwen3 and Gemma level models that run on mid-high end laptops and desktops can be pretty good. Not frontier grade, but shockingly competent for something that runs on a single PC. But the hardware you need to run those fast is at least $1000-$2000. Cheap hardware can run them, but slooooooow.
Also tried the "Locally Uncensored" setup on a 3060 laptop, which worked surprisingly well.