LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition — mentioned by yacine_

Wed, 26 Jul 2023 17:13:37 GMT

If you fine tune them to be task specific, they'll perform well. In my experience, this control loop is a better investment than "prompt engineering". (When I say task specific, I mean very task specific) GPT4 over the API is too fine tuned, which constrains its behavior. It fails to capture nuance in instructions. When you have the bag of weights, you can actually control your model. Having actual control over the model, and understanding the infrastructure that it's running on helps you meet actual SLAs. And it's cheaper, if you're not backed by infinite venture money.…

Paper and discussion history. A mention is not an endorsement.

LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition · HackerPapers discussions

LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition — mentioned by yacine_