Extracting Steering Vectors from J space

(darshanmakwana412.github.io)

19 points | by martianvoid 2 days ago

2 comments

  • a2ff6eeb0 38 minutes ago
    This sounds like a great foundation for an adtech startup.

    If you provide free chatbot services, but sell advertisers bids on which steering vectors to use to bias towards products, based on an embedding of the prompt, I bet you'd make a ton of money. For example, Coca Cola would bid on prompts about drinks, and bias towards mentioning Coke products.

    I wonder if you could also use a similar method to do product placement in GenAI images and videos, and whether ad revenue would be enough to offset the price of generation. Some ad bids can go pretty high...

  • lwarfield 42 minutes ago
    If the author would like, I self computed a j lens for the 27b version of the qwen model. I used it for my own exploration in this area, and can share it if you want.
    • schmorptron 29 minutes ago
      I'm not the author, but I would like! Is it feasible to run on the same hardware as the 27b model itself?
      • lwarfield 18 minutes ago
        The using a J lens is super cheap compared to inference. You basically add a single matrix multiply per layer. You probably wouldn't even notice the overhead in a good implementation.

        You should even be able to create a j lens from scratch, but it might take a while. I was able to do it in a few hours on an H100. Creating the J lens is basically the equivalent of calculating a few thousand training steps for a model (256,000 backprops in my case). I've got more details in a blog post:

        https://blog.lwarfield.dev/layer-scope/

        I'm currently at work and can't those matrixes up until I get home. I'll update this comment with a link later.

      • jasonjmcghee 17 minutes ago
        Fwiw you can just Google this for a model and often someone has done it

        https://huggingface.co/eyes-ml/Qwen3.8-27B_jacobian-lens