There's currently a project in progress called OpenAssistant. It's being organized by Yannic Kilcher and some LAION members, to my understanding. Their current goal is to develop interfaces to gather data, and then train a model using RLHF. You can find a ton of discussion in the LAION discord. There's a channel for this project.

> Their current goal is to develop interfaces to gather data, and then train a model using RLHF

Potentially naive question, as I don't have much experience with LLMs. Has anyone tried using existing SotA (paid) models like davinci / gpt3 instead of RLHF? They seem to be pretty good at a bunch of focused tasks, especially in few-shot. Does that make sense?

So using a big network to teach a small network? That's a thing people do. See teacher-student learning, and distillation.

ChatGPT is GPT3 + instructional finetuning + RLHF for alignment. If you're talking about using those models ro gather training data, that's against OpenAI TOS, so I've heard. The goal is to make something that isn't closed source, something you can run yourself.

I looked through their repo, but I'm not understanding something: what is the foundational model that they plan to use and where/how will the model be run?

Thanks for the hint: https://github.com/LAION-AI/Open-Assistant looks interesting :^)

From what I've heard, they want a model small enough to run on consumer hardware. I don't think that's currently possible (probably not enough knowledge capacity). But I haven't heard that a decision has been made on this end. The most important part of the project at the moment is crowdsourcing good data.

Wow, that seems awfully ambitious given that GPT3.5 requires something like 700GB of RAM and the apparent unlikeliness that SoTA model sizes will get smaller anytime soon. Interesting project to watch, though.

Well, there are projects like WebGPT (by OpenAI) that make use of external knowledge sources. I personally think that's the future of these models: moderated databases of documents. The knowledge is much more interpretable and modifiable that way.

If the goal is for a commercial endeavor I suggest bringing on an ML-specialized shop. Good ones can knock this out in a few sprints. PM me if you need more info.

less than 500MB is used for code learning, 690GB is used for culture, geography, history, fiction and non-fiction... 2GB for cats, 2GB bread, horses, dogs, Cheese, Wine, Italy, France, Politics, Television, Music, Japan, Africa. less than 1% of the training is on science and technology, i.e. 300MB is biology, 200MB chemistry, 100MB physics, 400MB maths...

I don't think there are any laws that protect their data in this way, except perhaps contract law because they have a hidden ToS that you have to accept to use their service. As long as you use it for free though, I'm not sure there is consideration, and well... I don't know how they would go about proving misuse or damages.

Certainly it would not be copyright law, given that GPT3 itself was trained on copyrighted data...

I sent you at chat

This may be the size of the datasets, but i it's hard to say how many parameters will be needed for a good llm that's just really good at explaining code.

There is a project called Petals that have BLOOM running for everyone to use. It's distributing the model over many machines and thus allows it to run on consumer hardware. There is a PoC chat at http://chat.petals.ml/

They just converted BLOOMZ and is currently setting up a network for that. That should be more suited for a chat interface. There's still missing gpu's though, so would be great with some more servers if people got some spare compute.

> See teacher-student learning, and distillation.

Thanks, I'll check it out.

If it was possible to try distillation on ChatGPT to produce smaller models which could run on mobile hardware, it would have been great!

Then we'd have to crawl all of stack exchange, all of wiki, and 1 terabyte of programming books... This "generalist NLP" is for article writing, for poetry.

I'm a big fan of teaching ChatGPT how to interpret graphs, the origin lines, to record in a vector engine that is couple with the NLP. For a coding engine, I believe NLP should be paired with a compiler, just like a maths specialized NLP should also have a mathlab type engine.

Well, can you just run it from an SSD, but more slowly?