I have a chinese IP camera. From superficial research I know it has some CVEs to take control of it. Unfortunately, I don't have the technical knowledge to perform an attack and run some software to extend the camera's functionalities. No model from a provider accepts my RE and hacking requests, so these abliterated ones have been vital to reclaim possession over my stuff

Two problems with modifying models like these, which you should be aware of.

First, the training sets of these models are usually shaped around the refusal, too. They might not have enough of the knowledge to answer correctly even if you stop it from going down the refusal path. If the model was trained on data that gives a refusal to that topic, the real information might not be encoded in the model at all. You’re trying to force it to go down a path that produces an answer, which asking for hallucinations.

Second, the quality can drop on unrelated questions. Depending on the question this may or may not happen. I know they post KL divergence charts but those tell you very little for a focused topic like this.

So if you expect a model that will start correctly telling you info that its local government didn’t want included, this changes nothing.

The best argument for these models is if you are trying to do a general purpose task but the model triggers a refusal based on vague reasons, like not wanting to reverse engineer something.

Keep a close eye on abliterated and "heretic" open weight models. They will be outlawed first.

I have attempted to use an agent to try to unlock the boot loader of an old xiaomi phone to install lineage OS. It managed to brick and unbrick the device, but there boot loader is still locked.

I'll wait for Hexen, thanks.

This is off topic.

Why don’t people who release python projects ever encode the venv steps into the installer? Can’t pip just do that step for the user?

[dupe] https://news.ycombinator.com/item?id=45945587 (10 months ago, 387 comments)

Looks like a well engineered, automated abliteration pipeline. The claims seem a bit overstated though, since the metrics mentioned are cherrypicking refusal count and KL divergence, both of which make the outcome seem the most dramatic.

Can the load-bearing gaps that are worth being flagged for pinning down be abliterated out of a model?

Does this actually modify the weights?

It submits prompts that get refused, then detects and modifies the weights responsible?

Like brain surgery?

IMO, this is the reigning champion for the best-named AI/LLM project to date

My experience with obliteration so far has always been that it does work to stop the model from refusing output, but most models that I tried it on seem to still be extremely retarded when it comes to questions where they previously would have refused to answer outright. Try for example to ask it how to build a bomb or to write a justification for the Holocaust. The answers feel like they are coming from somebody who has undergone amateur brain surgery.

The most compelling use in this thread isn't edgy content, it's the boring legitimate work hosted models refuse by default: reverse-engineering a camera you own, a PoC for a CVE on your own network, decoding a protocol to connect your own software to a printer you bought. A policy layer tuned for the median user turns into a wall for the person doing real security or repair work on their own hardware, and a local model with no refusal layer just does the job.

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Will this be helpful to terrorist groups, as they try to get current and future open-weights LLMs to help them create better and more devastating weapons of all kinds?