Nice, thank you! You're adding to my reading list, and I appreciate that! :) I'm still mulling over how difficult it would be to reimplement this with "stock"

Nice, thank you! You're adding to my reading list, and I appreciate that! :) I'm still mulling over how difficult it would be to reimplement this with "stock" llama.cpp. It feels like the first step would be to essentially get the "super-embeddings" for each prompt -- instead of grabbing just the text embeddings (which I understand is usually only the narrowest layer?) -- we would want to store off the activations for every layer. Then save them all to a list, average them together, and then figure out a way to use that to modify the weights of the model -- either at runtime (I imagine this much like a current guidance-vector would be loaded), or else (as you suggested) write a script to bake the modifications into the core model (but using multiplication rather than addition). Does that match your understanding? Thank you very much for helping me think this through!