It might seem like I’m entrenched in my position, but it’s quite the opposite — the only reason I’m doing this is because I really believe it’s the best outcome for devs in the long run. I’m open to changing my mind and pulling the plug in everything.

I’ll read over your essay and give it some thought. There are a bunch of subtle aspects to consider; I’ve been thinking it over for about four months now and still haven’t covered all the territory yet.

It feels like this may be one of the most important decisions going forward — both from an intellectual property point of view, and an individual rights perspective. E.g. you mention that it’s civil disobedience to share the weights, but it feels like if someone is claiming to do open science (LLaMA), sharing the research materials is the minimum requirement. Plus look how it’s benefited them; they’ve captured most of the open source LLM mindshare. So it seems likely that this will lead to more open source work in the long run, not less.

Feel free to chat! You can DM me on Twitter or email me. I’ve been in the hospital with my wife for 7 weeks, with two to go, so I’ve been a bit less responsive than I usually am.

~I’ll read over your essay~

I'l get an LLM to summarise your essay...

> otherwise we're just going to see everything good disappear behind trade secret

We will see that anyway. All the code I work on commercially is copyrighted and yet a trade secret. Existence of copyright (with the exception of copyleft, but that's subversion) didn't help software to be open sourced.

IMHO allowing models to be copyrighted is basically 18th century enclosures again.

> All the code I work on commercially is copyrighted

What do you mean by that? Do you continuously copyright the changes?

Yes, more or less. I am not really sure why we legally do that, I believe it's just another protection in case someone actually copies the code.

I mean everything is copyrighted anyway. It's harder to give up copyright than to keep it, so the main point is despite copyright protections, most companies do not publish their code publicly at all. For code that has to be pushed to clients, most companies even take efforts to obfuscate it.

Even RMS himself prefers the abolition of copyright over the existence of the GPL.

Besides, it's already pretty unambiguous that weights are not copyrightable: they're a result of a mechanical process. The only original creative input that goes in to the weights is the unfathomable amounts of content scraped from other sources that aren't the authorship of the models. The objective of the gradient descent is simply minimizing loss on the training data.

Facebook doesn't own the llama model weights any more than the Bridgeman Art Library practically owns the paintings of European masters because they made quality scans of them. ( https://en.wikipedia.org/wiki/Bridgeman_Art_Library_v._Corel.... ), or any more than Rural Telephone owns the phone directory ( https://en.wikipedia.org/wiki/Feist_Publications,_Inc.,_v._R.... ).

Trying to make model weights copyrightable is going uphill, and I don't see how you get there without first establishing that the these LLM are unlawful derivatives of a countless number of copyrighted works along the way. Doing so would probably create a immediate monopoly for legally created LLMs for the hand full of corporations with quasi-monopoly content hosting services (facebook, google, etc) that can (and/or already have) stuffed licensing into their terms of use.

Do you want a cyberpunk dystopia? I think creating an AI monopoly is how you get a cyberpunk dystopia -- and the two ways we end up with one is either outright restrictions on private development of AI like some have been lobbying for and the other is the extension of copyright so that only a few entities can get access to enough of other people's data at a low enough cost to train them.

Reading these discussions with interest as I am in the process of making and training my own personal model using my 31+ archive of photography, pictures taken and owned by be, which goes against this idea that models are not trained on data owned by the companies doing the training. While this is all for my own personal interest and use, how would the idea that the weights cannot be copyrighted affect my rights on the model if I were to release the whole thing for use?

I'd be interested to know how your model performs if it is trained only on your own work.

Assuming you haven't taken a photo a second for your entire life, then I suspect you'll struggle to make something even close to what's available publically, due to lack of training data.

Obviously not, but the point isn't to make a public model, but to make something out of my own - my question was how does ownership work? At some point, somebody is going to be making something out of their own massive archive.

How big is the archive? These models are typically trained on at least 100M images.

There's around 1M images in there, I was wondering whether the labelling and object detection would be more important than the quantity.

1M images is probably enough to do something, this user for example trained a diffusion model from scratch using 1.5M images and a 3090: https://medium.com/@enryu9000/anifusion-diffusion-models-for..., the quality of course is not excellent but it's something. I suggest to train a 4x64x64 diffusion model using the new SD XL VAE (it's a really good f8 VAE so it can encode, for example, images from 3x512x512 to 4x64x64), if the images have captions then I suggest using a CLIP text encoder as it was already trained on image text pairs, it would probably be much easier to use by a diffusion model trained on only 1M images instead of other text encoders like T5 that have better text understanding but they have never seen an image.

Quantity is massively more important, to the point that you get much better results by using a larger dataset with machine generated labelling (or object detection if that's what you're building) than using a smaller one with even expert labelling.

That said, if you've got a million photos you could probably do some pretty interesting things with very large scale fine tuning, or if you know many other people who have similar stockpiles of photos you may be able to get an entry-level dataset together if you all pool it.

> If you’re sceptical, go look at one of the forums where people are building derivatives of Stable Diffusion (possibly NSFW, I’m not providing any links).

Does anybody have a link to a relevant discussion here? I would like to read about the creative process that goes into defining model weights, and how it differs from the mechanical output of running the training algorithm.

There’s no use living a lie. Training a model is not an act of creative expression and cannot give you authorship of the weights. Enforcing GPL if you don’t have IP no better than any other copyright troll…