[Garcia98]

I disagree, they made the decision to use datasets with restrictive licensing, jumping the alpaca/gpt4all/sharegpt bandwagon.

They also chose to toot their horn about how open-source their models are, even though for practical uses half of their released models are not more open source than a leaked copy of LLaMa.

[peterstjohn]

So just use their base model and fine-tune with a non-restrictive dataset (e.g. Databricks' Dolly 2.0 instructions)? You can get a decent LoRA fine-tune done in a day or so on consumer GPU hardware, I would imagine.

The point here is that you can use their bases in place of LLaMA and not have to jump through the hoops, so the fine-tuned models are really just there for a bit of flash…

[jimsimmons]

Looks like you’re seeing the glass as half empty here. Not sure if arguing here was more time efficient than just running the eval on the other set of weights.

*I wish I understood these things well enough to not have to ask, but alas I’m just a basic engineer

[Garcia98]

I use a GPU server and runtime is not free unfortunately.

[jimsimmons]

Ah no worries then. Thanks for your datapoint regardless