[minimaxir]

That blog post demonstrates that it's not "easily" finetuneable, just possible to finetune. There's many technical considerations even beyond hardware (dataset formatting, training hyperparameter nuances) that do not make it accessible to the newbie experimenting with LLMs.

It's a rabbithole, and unfortunately there's no good shortcuts.

[ttt3ts]

It isn't script kiddie level but it isn't hard I finetuned a 15B parameter reddit bot with an afternoon of time and a day of training on a 3090. Bot got a few thousand Karma in a couple of days before I turned it off (proof of concept done).

If all you have is an M1 or whatever, ya, you need a real workstation and depending on your use ChatGPT might be cheaper/better.

[Solvency]

Why have there been thousands of overnight AI/GPT startups and products in the last few months and NOT a single simple intuitive "fine tuning wizard" app? That seems like such an obvious glaring gap.

[minimaxir]

Because the ChatGPT API (and analogous competitors) is cheap enough that it's both faster and more cost effective to just use it instead instead of using your own model, with maybe some shenanigans to handle its shortcomings without increasing cost much if at all.. And that was before gpt-3.5-turbo-0613, which dropped the price more and is about 2-3x faster.

There are startups that do finetuning on your own data, but with zero hints on how to preprocess your data and absurd costs (both upfront training and GPUs for serving inference) that's it's extremely difficult to argue from a customer business perspective compared to just using an API.

[shapefrog]

> Why have there been thousands of overnight AI/GPT startups and products in the last few months and NOT a single simple intuitive "fine tuning wizard" app?

Vapourware GPT startup inc is valued at $2bn the afternoon after you form the company and buy your first macbook.

Actual usage of Ai, fine tuning etc. I can offer you $100,000 for 30% of your company if you can demonstrate a fully working product.