It's been a long time since I've researched LLMs, but I once read somewhere that they were capable of identifying the nodes in the neural net that were involved in generating an answer. If they remove those nodes from the model or zero the weights, then the NN loses whatever information was used. They call it "concept erasure" IIRC.

That's a lot more hazy than what I'd thought you meant. Papers exist for all kinds of things, but that doesn't mean they all really work, or that they're all being used by a major company in production. I think this is what you saw, and it's for fine-tuning low rank adaptations. Having read plenty of papers like this, I'd bet it's a lot less effective in practice. Anthropic, the "AI Alignment" company, has more recent research that's a lot less ambitious, and even that's basically just theoretical.

Any case, "just a jailbreak prompt" is not what I'm referring to. When a model is online, they prevent you from editing the things that it says. With an open source model, you can directly edit the things that it is saying, providing affirmative responses from "its own mouth" as the lead-in to a task completion. LLM refusal training can't handle that, which is why the online interfaces are so restrictive.

Moreover, I don't think you need a $15k computer to run an LLM, especially in the era of quantization. You can run the lightest DeepSeek model on your laptop, or on a cheap Colab server ($10 a month). I've done this myself a while back (though not with DeepSeek), and I'd highly encourage you to try it out.