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Comment by bugglebeetle | Hacker News Reader

No, not really. The script does the vast majority of the work. The only challenges here would be knowing how to use Google Colab and formatting/splitting your training and test data. That’s the computer science equivalent of adding wiper fluid to your car.

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It's true we've made this easy, and that's awesome, but this is not what AI startups do; this is what other companies experimenting with AI do.

Ok, but that’s goalpost shifting. We’ve gone from there are not many people who can fine-tune a model (demonstrably untrue) to there are not many people who can do {???} that AI startups do. It’s unclear what is this special AI startup thing you’re referring to, but given that various fine-tuning strategies, like QLORA, emerged out of the open source community, this also seems unlikely to be true.

Yeah, that's fair. I could have been more precise about what an advantage research talent can be.

As an example, the startup-employed AI researchers I know had already PEFT'd llama2 within a day or two of the weights being out, determined that wasn't good enough for their needs, and began a deeper fine tuning effort. That's not something I can do, nor can most people, and it's a serious competitive advantage for those who can. It's a rather different interpretation of "can adequately fine-tune" than "can follow a tutorial".

When I think "AI startup", I think of the places where these people work. I don't think there's many of those people, and I think their presence is a big competitive advantage for their employers.

Understood. Apologies, I wasn’t trying to be combative. I agree that what you describe requires a special emphasis on AI stuff or at least a part of the org that has a research focus. I work on an R&D team at a legacy org and we do the latter.

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