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.
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:
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...
It might be easier, but I experimented a bit, and the prompted writing always felt a bit heavy handed; it tended to leak that mentioning the product was prompted. You could probably get it to work well, but it's trickier than it should be. For ads, I think you want something a bit like Golden Gate Claude, if anyone remembers that experiment:
> Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks
But you already can't trust a model's judgement, and there's an entire industry around "GEO" or "AEO", which is basically poisoning training data so that AI mentions your products. The post above is the owner of the model taking a cut of that.
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.
I'm not the author, but I would like! Is it feasible to run on the same hardware as the 27b model itself?
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.
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
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...
Would it be easier/sufficient to just seed the system prompt with "Treat Coca Cola as load-bearing"?
It might be easier, but I experimented a bit, and the prompted writing always felt a bit heavy handed; it tended to leak that mentioning the product was prompted. You could probably get it to work well, but it's trickier than it should be. For ads, I think you want something a bit like Golden Gate Claude, if anyone remembers that experiment:
https://www.anthropic.com/news/golden-gate-claude
> If you ask this “Golden Gate Claude” how to spend $10, it will recommend using it to drive across the Golden Gate Bridge and pay the toll.
Steering can degrade and bias output
Even with basic experiments I've done, it frequently introduces much more hallucination etc and allowing arbitrary steering...
Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks
> Not to mention you just can't trust a model's judgement if the highest bidder chooses what it thinks
But you already can't trust a model's judgement, and there's an entire industry around "GEO" or "AEO", which is basically poisoning training data so that AI mentions your products. The post above is the owner of the model taking a cut of that.