For those wondering why this is interesting: This technique is being used to reproduce[0] the Alpaca results from Stanford[1] with a few hours of training on consumer-grade hardware.
I believe there will soon be a cottage industry of providing application-specific fine-tuned models like this, that can run in e.g. AWS very inexpensively. The barrier today seems to be that the base model (here, Meta's LLaMA) is encumbered and can't be used commercially. Someone will soon, I'm confident, release e.g. an MIT-licensed equivalent and we'll all be off to the races.
I believe there will soon be a cottage industry of providing application-specific fine-tuned models like this, that can run in e.g. AWS very inexpensively. The barrier today seems to be that the base model (here, Meta's LLaMA) is encumbered and can't be used commercially. Someone will soon, I'm confident, release e.g. an MIT-licensed equivalent and we'll all be off to the races.
[0] https://github.com/tloen/alpaca-lora
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html