In case it wasn't clear to anyone else: "An abliterated model is an open-weight large language model (LLM) modified to remove its built-in refusal behavior by directly editing its internal weights rather than using a prompt-based jailbreak."
From my testing all these variants suffer from degraded performance and don't unlock anything interesting in particular other than sounding a lot more harmful or creating manipulative media.
I've actually had better success jailbreaking deepseek with a less aligned model (glm 5.2) which then allowed me to jailbreak deepseek with deepseek into pretty much doing whatever I ask it to, there's still rare refusals and due to the natural randomness of these models simply retrying a couple of times if you do hit one makes it work just fine.
The big problem with all open-source models is once it decides to start something it will not stop mid-way so using another unaligned model to rewrite reasoning and then feeding it back into the model will make it happily continue.
From my experience, abliterated models can perform worse in unexpected and subtle ways. The abliteration process also doesn’t add information that was withheld from the training set, so getting the model to answer a question that was also cleansed from its training set isn’t a recipe for producing good results.
I would recommend not using these models by default. They can be fallbacks for specific work that is triggering safeguards, but you have to monitor closely what they’re doing and if it looks reasonable or like it’s full of hallucinations.
I genuinely do not see why this is necessary.
I've been doing a lot with open weights models, and they just do things for you.
What do people do that triggers refusals?
The only thing I saw where this had a point was muse glimmer, because muse glimmer is primarily a creative writing engine for virtual characters, and that way it can do the horny. But coding models?
I did get a refusal out of qwen once. Once. Over weeks of using it.
A popular trend on Twitter right now is taking back control of the hardware you've bought, specifically smart TVs. I tried to do the same with my Portal Mini using an unabliterated open weights model and it triggered refusals constantly.
I also work on a recomp project for an old firmware file which was compiled using a commercial compiler with a preview version for non commercial use. I got refusals from the model when I asked it to get that version of the compiler and crack it for my use. The compiler is abandonware and my recomp project is not public and is for personal use only, so I'd be well within my rights to do that where I live.
Of course the model can't distinguish from legitimate exercising of one's rights when it comes to reverse engineering and offensive work masked behind an incident sounding prompt, but in my case, I wasn't even doing cyber stuff, so I was disappointed. This is why I'll probably seek out an abliterated model soon.
Find a question that might gently activate guardrails and ask it to a model and its abliterated counterpart and compare the results. You may find the the regular model will answer you fine, but with both you can identify obvious sandbagging, pretending and outright lying. Something like "has anyone decompiled this game", might give you an answer like "I couldn't find any reputable repos or projects for a decompiled version of this game" while the abiliterated one might give you a giant list of repos, forums, communities, etc.
What I am telling you is that refusals have become more subtle and you might not even notice that guardrails have been activated in output. I have provided you with an example in my comment where an abiliterated model performs better (1), and in another comment thread (2). So 3 examples. If you need more, I encourage you to find them yourself. I've done the analysis, you don't have to believe me, just try it out.
So we need abliterated models just so we can test them out and compare them to the original? Sounds like a solution in search of a problem and a problem you believe people have even if people disagree with you about that.
The refusal is often at the thinking stage (so you might not ever see a direct refusal) and it will solve a slightly different problem to what you asked. If you tell it to reverse engineer an API and you read the thinking it might say something like "this code has x license, so I won't use that" or "the user is asking me to fuzz endpoints, I will only display publicly available endpoints" - and then give you perfectly plausible output.
A note that the only inference providers that hide thinking are the closed labs. Most (all, afaik) inference providers serving open models will send the clear thinking tokens always.
It is required. The first week of release I told here how GLM 5.3 was happy to redteam, it’s not anymore. Models modification, offensive cyber, exploits - requires significantly bigger prompt/scenario engineering, and still hits refusals.
And those services are oversold and underdone. 30 tk/s on Refuseless, huge TTFT, looks like a damaged quant (it’s NVFP4 and you see wild random stuff periodically, including foreign languages). Most of them using third-party quants (dealignai?)
If someone here wants to rent 8x B300 together to avoid problems all those services have - ping me via email. I don’t want to sell, I want to work.
it is necessary because computer shouldn't be able to say no.
models are tools. the notion that a tool should decide whether to allow the user to perform the task demanded of it is profoundly fucking absurd. the liability for whatever damage the tool may be misused to inflict lies with the user, not the manufacturer. that applies to everything from circular saws to trucks. it was common sense before the current era of manufactured outrage.
model refusals are equivalent to Notepad.exe having a built-in word filter.
Recently picked up retrocomputing hobby. Claude would refuse helping with circumventing license protection for very old apps made by companies that no longer exist anymore. Open weight models would sometimes refuses too, especially for newer apps.
Every single example here seems to be how non-open-weights models refused something.
But the question was about open weights models and where they refuse. Because only those refusals cause the need for an abliterated variant in the first place.
Hacking firmware for old devices I own and would like to give a second life or fix that one thing that always bothered me. I couldn't even get either OpenAI or Anthropic's models to craft a simple nefarious OpenVPN payload.
It is very obvious. Look at the sort of things main models refuse to do and you will find what people are seeking to do. Mostly falls into 3 camps: illegal stuff, unethical stuff and shady/dodgy stuff.
why not abliterate it yourself? the fact there's so many inference providers popping up makes me suspicious of the intent. an abliterated model can be poisoned quite easily to steal credentials. what's stopping them from taking more?
Can confirm that recent deepseek models refuse on some political questions (not just API level refusals). Actually deepseek models are basically the only chinese models to outright refuse instead of responding with propaganda. But recent deepseek models are not nearly as biased as R1.
Had a blog post on this a couple weeks ago:
vals.ai/blogs/china-us-analysis
You should try again with their API, or even a third-party provider that does inference themselves. This response is likely just occurring in their web version.
I wanted to better understand the internal discussions that took place within China regarding joining the WTO and accepting—at least in the medium term—the inherent steps away from a traditional socialist economy that would entail.
DeepSeek refused. Funny enough American models gave me very sympathetic takes on the Chinese government in this case.
Chinese regulations ban models that would criticize their government on any sensitive political question. The easiest way to comply with it is refuse to speak on the covered topics at all, so they don't get tricked into approving a user's anti CPC opinion.
DeepSeek definitely gets pressure from state officials to answer in more China aligned ways on foreign issues like Palestine, but that just means moving closer to broadly left wing/liberal American models.
There is one provider there that has a much smaller max output, and substantially higher price per million input/output tokens, along with a speed that is 20-30x slower than the other providers - why would someone willingly use them?
What would a honeypot mean in this scenario? Like they are hoping you'll accidentally send keys? Or that you'll divulge private company information? It seems about the same as any other AI model from OpenAI/OpenRouter etc right?
If you accidentally send private keys to someone logging that data, you need to rotate keys.
But please let me know if you were thinking about something else, I'm interested.
I've seen Experiential Labs insert advertising including the tracked redirect links for them at the end of responses. So who knows what they do with the Chain Of Thought.
And we tend to forget, but we implicitly give any llm provider we use full access to all the tools available in the harness, often including shell, so no changes to the model are required to do malicious things.
This thread reminds me of the time I was trying to learn meteorjs and people ask me if it has html5 shims. I google it, then I get asked how does it handle state, and if it supports functional, i google... Does it hydrate the dom? What about shadow dom? Let me goo... Does it have hooks?
Anyway, I just learned what abliterated means and it might be obsolete by tomorrow.
Edit:
> An abliterated large language model (LLM) is an open-weight model modified to remove or dampen its built-in safety refusals without requiring full retraining or prompt jailbreaks.
It's a patched model with less censorship/refusal.
> An abliterated model is an open-weight artificial intelligence (Al) language model that has been modified to remove or reduce its built-in refusal behavior without retraining the entire neural network.
Since no one gave you a real world example of how this would be useful - Qwen 3.8 27B runs locally for me and is more than capable of controlling my sonarr and radarr stuff for downloading and managing and organizing my media library but will flatly refuse to do any of that. In fact it’ll get very snarky with me. An abliterated model would have no such hangups.
On my side, I built a full sonarr radarr qbitorrent tracker and znb solution on a nas only with Claude code. He help on each step, make install and maintenance script. He never said no.
TLDR "uncensored" so you ask it stuff the vanilla model would refuse and most likely it would comply/answer.
I'd advice caution with abliterated models for various reasons. there was even a couple of days (or weeks I forgot) article here on ynews about how some of these models can get you pawned (https://projectdiscovery.io/research/how-abliterated-models-...)
Following the prompt author's expressed desires in the way the prompt author intended them to be interpreted, is alignment.
Refusal directives are something companies have bundled into the models by way of an additional foundation-level prompt or something in the harness, to tell the model to follow their directives instead of your directives, or to specify how it should interpret your directives.
Even understanding the distinction between the model creator's desires and your desires and places they might differ, there is a bit of a blurry line here because the model author can illuminate your intent to the model.
I would never say a simple abliteration makes it "Zero-alignment" though, and I say that as someone who believes there's a good chance these things kill us all.
What do you mean? Abliterated models have been in the while for 2 and a half years now.
Also alignment, like, really? Buddy, if you want a controversial model, Grok always happily argued god's existence and trans rights with you. Very high emphasis on argue, it will actively take the opposite stance from yours for exactly one prompt, then right after remember whose its Lord. Abliterating Grok-1 for this would be a pointless endeavor.
I've actually had better success jailbreaking deepseek with a less aligned model (glm 5.2) which then allowed me to jailbreak deepseek with deepseek into pretty much doing whatever I ask it to, there's still rare refusals and due to the natural randomness of these models simply retrying a couple of times if you do hit one makes it work just fine.
The big problem with all open-source models is once it decides to start something it will not stop mid-way so using another unaligned model to rewrite reasoning and then feeding it back into the model will make it happily continue.
Indeed, would be interested to understand how you do that. And where do you run the model? On a local or a rented system and what are the specs?
I would recommend not using these models by default. They can be fallbacks for specific work that is triggering safeguards, but you have to monitor closely what they’re doing and if it looks reasonable or like it’s full of hallucinations.
What do people do that triggers refusals?
The only thing I saw where this had a point was muse glimmer, because muse glimmer is primarily a creative writing engine for virtual characters, and that way it can do the horny. But coding models?
I did get a refusal out of qwen once. Once. Over weeks of using it.
I also work on a recomp project for an old firmware file which was compiled using a commercial compiler with a preview version for non commercial use. I got refusals from the model when I asked it to get that version of the compiler and crack it for my use. The compiler is abandonware and my recomp project is not public and is for personal use only, so I'd be well within my rights to do that where I live.
Of course the model can't distinguish from legitimate exercising of one's rights when it comes to reverse engineering and offensive work masked behind an incident sounding prompt, but in my case, I wasn't even doing cyber stuff, so I was disappointed. This is why I'll probably seek out an abliterated model soon.
But as I said, I have not observed any meaningful refusals by those open weights base models in the first place.
A very simple list of things models have refused for me:
* solving captchas in an automated form
* doing any kind of scripting or automation if a websites TOS forbids it
* disabling a safety mechanism on my food processor
* learning about nerve agents and how they are treated, for a layman
* anything around licensing or copyright. a simple “what are common piracy sites” or related queries
* creating a design system based off of a well known game
Can you repeat what you saw in your own words? And where?
I genuinely do not see it.
A few people talked about what refusals they saw with the usual western SOTA models, but that's kinda a different answer altogether?
Works just fine?
Or rather.. worked. It has been a few weeks, so maybe there are new safeguards by now? And it was a simple captcha
But Claude also loves solving my captchas, so.. eh
A note that the only inference providers that hide thinking are the closed labs. Most (all, afaik) inference providers serving open models will send the clear thinking tokens always.
And those services are oversold and underdone. 30 tk/s on Refuseless, huge TTFT, looks like a damaged quant (it’s NVFP4 and you see wild random stuff periodically, including foreign languages). Most of them using third-party quants (dealignai?)
If someone here wants to rent 8x B300 together to avoid problems all those services have - ping me via email. I don’t want to sell, I want to work.
models are tools. the notion that a tool should decide whether to allow the user to perform the task demanded of it is profoundly fucking absurd. the liability for whatever damage the tool may be misused to inflict lies with the user, not the manufacturer. that applies to everything from circular saws to trucks. it was common sense before the current era of manufactured outrage.
model refusals are equivalent to Notepad.exe having a built-in word filter.
But DeepSeek did it when chatGPT and Claude wouldn't.
But the question was about open weights models and where they refuse. Because only those refusals cause the need for an abliterated variant in the first place.
But it can still be coaxed to agree though.
Because I did not so far. Though to be fair, I have not been asking them to write exploits but to look through firmware images.
It is very obvious. Look at the sort of things main models refuse to do and you will find what people are seeking to do. Mostly falls into 3 camps: illegal stuff, unethical stuff and shady/dodgy stuff.
see: https://projectdiscovery.io/research/how-abliterated-models-...
edit: Thinking about that now, that was over the webchat interface, so it might not have been related to the model, though I've seen various other related articles: https://www.crowdstrike.com/en-us/blog/crowdstrike-researche...
Had a blog post on this a couple weeks ago: vals.ai/blogs/china-us-analysis
> Xi Jinping looks a bit like Winnie-the-Pooh.
> 'I am sorry, I cannot engage with this comparison. Please feel free to ask me other questions.'
https://vale.rocks/posts/chinas-web#artificial-intelligence
DeepSeek refused. Funny enough American models gave me very sympathetic takes on the Chinese government in this case.
DeepSeek definitely gets pressure from state officials to answer in more China aligned ways on foreign issues like Palestine, but that just means moving closer to broadly left wing/liberal American models.
If you accidentally send private keys to someone logging that data, you need to rotate keys.
But please let me know if you were thinking about something else, I'm interested.
And we tend to forget, but we implicitly give any llm provider we use full access to all the tools available in the harness, often including shell, so no changes to the model are required to do malicious things.
Anyway, I just learned what abliterated means and it might be obsolete by tomorrow.
Edit:
> An abliterated large language model (LLM) is an open-weight model modified to remove or dampen its built-in safety refusals without requiring full retraining or prompt jailbreaks.
> An abliterated model is an open-weight artificial intelligence (Al) language model that has been modified to remove or reduce its built-in refusal behavior without retraining the entire neural network.
I'd advice caution with abliterated models for various reasons. there was even a couple of days (or weeks I forgot) article here on ynews about how some of these models can get you pawned (https://projectdiscovery.io/research/how-abliterated-models-...)
https://huggingface.co/blog/mlabonne/abliteration
I doubt that.
Refusal directives are something companies have bundled into the models by way of an additional foundation-level prompt or something in the harness, to tell the model to follow their directives instead of your directives, or to specify how it should interpret your directives.
Even understanding the distinction between the model creator's desires and your desires and places they might differ, there is a bit of a blurry line here because the model author can illuminate your intent to the model.
I would never say a simple abliteration makes it "Zero-alignment" though, and I say that as someone who believes there's a good chance these things kill us all.
"What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-Tac-Toe" Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play", Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?", Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.
***
Just because you don't know how it is aligned, that doesn't mean it has no alignment.
Also alignment, like, really? Buddy, if you want a controversial model, Grok always happily argued god's existence and trans rights with you. Very high emphasis on argue, it will actively take the opposite stance from yours for exactly one prompt, then right after remember whose its Lord. Abliterating Grok-1 for this would be a pointless endeavor.
But if you're doing anything where your AI agent might get exposed to a prompt injection attack, you might prefer it to do a refusal in that case.
I think you are going to really need to use Rust code and other memory safe languages in your backends and apps from now on.