[diggan]

> How does it help to know the steps when creating a base model still costs >tens of millions of dollars?

You can still learn web development even though you don't have 10,000s of users with a large fleet of servers and distributed servers. Thanks to FOSS, it's trivial to go through GitHub and find projects you can learn a bunch from, which is exactly what I did when I started out.

With LLMs, you don't have a lot of options. Sure, you can download and fine-tune the weights, but what if you're interested in how the weights are created in the first place? Some companies are doing a good job (like the folks building OLMo) to create those resources, but the others seems to just want to use FOSS because it's good marketing VS OpenAI et al.

[Miraste]

Learning resources are nice, but I don't think it's analogous to web dev. I can download nginx and make a useful website right now, no fleet of servers needed. I can even get it hosted for free. Making a useful LLM absolutely, 100% requires huge GPU clusters. There is no entry level, or rather that is the entry level. Because of the scale requirements, FOSS model training frameworks (see GPT-NeoX) are only helpful for large, well-funded labs. It's also difficult to open-source training data, because of copyright.

Finetuning weights and building infrastructure around that involves almost all the same things as building a model, except it's actually possible. That's where I've seen most small-scale FOSS development take place over the last few years.

[fzzzy]

This isn't true. Learning how to train a 124M is just as useful as a 700B, and is possible on a laptop. https://github.com/karpathy/nanoGPT

[Miraste]

To clarify my point:

Learning how to make a small website is useful, and so is the website.

Learning how to finetune a large GPT is useful, and so is the finetuned model.

Learning how to train a 124M GPT is useful, but the resulting model is useless.

[diggan]

> Finetuning weights and building infrastructure around that involves almost all the same things as building a model

Those are two completely different roles? One is mostly around infrastructure and the other is actual ML. There are people who know both, I'll give you that, but I don't think that's the default or even common. Fine-tuning is trivial compared to building your own model and deployments/infrastructure is something else entirely.