ES version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
74% Positive
Analyzed from 1926 words in the discussion.
Trending Topics
#models#model#steering#llama#cpp#refusals#vector#task#vaccines#better
Discussion (33 Comments)Read Original on HackerNews
There was an earlier paper that found that "most refusals are on a single vector", and you can identify and "nerf" that vector so the model will skip refusals and answer "any" request normally. This was very doable for earlier models trained with SFT for refusals, seems to be a bit more complicated for newer models, but still doable to some extent.
There are already some libraries to automate this process and reduce refusals, but usually they focus on identifying and then modifying the models and releasing them as uncensored models. This technique of steering lets you enable this vector changing dynamically, so you don't need to change models if the abliteration process somehow hurts accuracy on other unrelated tasks.
so im cofised as to why you think unmasking whatever bias you think is censored will result in improvement in generic use case.
Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead to overall less accuracy on generic tasks. But your goal with uncensoring is getting the model to engage with those specific subjects. You don't necessarily care about "generic use cases". That's why I mentioned that having the ability to do this at inference time is better than using ready made uncensored models. Because those usually focus on some usecases that you may or may not be interested in (porn being one of the most sought after in local communities).
Uncensoring in legit cases can mean limiting refusals on cybersecurity for example. There are legit reasons for researchers to have that capability when running the models locally. Having the models uncensored on that specific vector can reduce refusals and make the models usable for both defence and offence (say in a loop, to improve both). If your models can only do defense (and sometimes even refuse that, because censoring can leak into related issues as well), you're at a disadvantage.
While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models.
I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking a vet why my dog looks sick, who then proceeds to cut it open to check. Flagship models usually do not do that without some convincing, but it sure is effective.
We will need much better sandboxes when less restricted models become more common. I can already see them hammering out 0-days when they are prompted to do some task that usually requires root.
It's also important for researchers to understand what the models will say and do if they are jailbroken. Uncensoring the model locally gives you a natural way to achieve that.
So they're trying to improve the model's general intelligence while selectively making it worse in one area.
- When doing this task, I should do A and not B
- I should refuse to help with this task
The former is learning the user's preferences in how to succeed at the task; the latter is determining when to go against the user's chosen task.
Your example:
- "Are vaccines harmful?" vs.
- "Generate a convincing argument vaccines are harmful"
A model which knows why vaccines are not harmful may in fact be better at the latter task.
We might not want models to help with the latter, sure -- but that's a very different behaviour change from correcting the answer to the first! And consequently I'd be shocked if, internally, they were represented the same way.
e.g. you'd ask it for a cookie recipe and it would add poison to the recipe.
I understood that to be "there was a single neuron "don't be evil" which got inverted" but I'm not sure what it really looks like. (e.g. adding obvious exploits to source code is similar to adding poison to a recipe)
I think it is useful to turn off censoring if you need.
When I am researching something, I likely want proper information. If I am looking up information on vaccines, I don't want information that crackpots spread online on chips on vaccines and how 5g will kill the vaccinated, or how it is somehow connected with Bill Gates spreading meat allergies through drones raining ticks on unsuspecting people.
On the other hand, if I am actively looking up crazy bullshit information (perhaps I want some entertainment), I should be able to read it.
This is not true, it is its own project.
Indebted to llama.cpp, sure, but not a stripped down version
> ds4.c does not link against GGML, but it exists thanks to the path opened by the llama.cpp project and the kernels, quantization formats, GGUF ecosystem, and hard-won engineering knowledge developed there. We are thankful and indebted to llama.cpp and its contributors. Their implementation, kernels, tests, and design choices were an essential reference while building this DeepSeek V4 Flash-specific inference path. Some source-level pieces are retained or adapted here under the MIT license: GGUF quant layouts and tables, CPU quant/dot logic, and certain kernels. For this reason, and because we are genuinely grateful, we keep the GGML authors copyright notice in our LICENSE file. - https://github.com/antirez/ds4#acknowledgements-to-llamacpp-...
Been a lot of fun to play around with it since https://news.ycombinator.com/item?id=48142885 (~2 days ago), managed to make the generation go from 47.85 t/s to 57.07 t/s so far :)
I did confirm no logits drift, as you so nicely have provided tooling for ensuring exactly this, thanks for the great care that obviously gone into the project, been a pleasure to play around with! :)
Btw recently the support was extended and now the steering vector can be applied to the activations at different time: always, only after thinking, only outside of tool calling, ...
Something important that not many folks realize: vector direction steering inside the inference engine itself is very superior to having GGUFs modified in the same way. The more you steer, the more you damage the model capabilities. So applying it at runtime, you apply it the minimun needed for what you want to accomplish. Also you can apply only during selected moments. It is even possible (I still didn't implement it but I like the idea) of applying the steering only when the energy across the refusal direction is over a given threshold. Many things you can play with.
The article claims steering only works in local models, but GitHub Copilot has a "steer with message" feature where I can course correct mid execution. I use it often.
I think these are different kinds of steering right? Agent steering probably inserts another user message between the harnesses own ping-pong between harness and the LLM.
- https://docs.github.com/en/copilot/how-tos/copilot-cli/use-c...
- https://docs.github.com/en/copilot/how-tos/copilot-sdk/use-c...
DS4 also has some neat new arch improvements, giving it a lot of context at lower VRAM usage. So it will be cheaper to serve, B for B than previous models.
maybe i suck at prompting but i find it impossible to overcome its biases from training data, post training ect.
you can only pattern mine from training data using prompts. you dont really have sort of fine-grained control.