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I'm curious about it too. I think there are two bottlenecks, one is that training a relatively large LLM can be resource-intensive (so people go for RAGs and other shortcuts), and making it finetuned to your use cases might make it dumber overall.


> making it finetuned to your use cases might make it dumber overall.

LoRa doesn't overwrite weights.


Do you need to overwrite weights to produce the effect I mentioned above?


Good point




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