We can expect an explosion of LLMs using this technique similarly. And later at some point the degradation of quality perhaps. Or may be all those LLMs will be just saying the same things at that point.
We can expect an explosion of LLMs using this technique similarly. And later at some point the degradation of quality perhaps. Or may be all those LLMs will be just saying the same things at that point.
What i want when i merge such models is a model that can generate in the artsyle of either fiona and james independently or a mix of both if i specify both keywords in the same prompt.
Currently, if you merge these 2 models and generate "busy city street, fiona staples style", you will not get a model that can generate works in fiona's style, you will just get a model that will generate an odd mix of fiona and daly even if you only specify one of them.
It means you either need to train a million different models for a million different concepts with no chance of cross usage (e.g x person wearing y clothes in z style will not be possible) or train those concepts at the same time, which becomes very cumbersome requiring a retrain of n+1 concepts on a fresh model anytime you want to introduce a new concept.
Oh, and training on x then training on y doesn't work in practice either because the model will mostly forget x learning y.