Why would you want to change weights directly versus running through a new training cycle? For example, I have collected a bunch of data I would like to train weights that I use local inference for. I figured a LoRa would be the way to go, perhaps be more cost effective and generalized too.
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Because text-to-weights is a learnable function. Everything else seems to be. So, why not? Why train weights if you could just prompt for them. If we managed to train image edit models, then why can't we train model-edit models?
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Because training is more expensive I guess
Something someone else brought up in one of the previous threads was to identify the refusal routes and remove them.
It would take a lot less FLOPS presumably
Speed, instant finetunes? Highly optimized iteration cycles? The applications are the same for any reason why you might want local llms. This is crazy exciting.