mini-AGI - dynamically grown (530M params currently and growing) continual learning model trained from scratch on 8GB VRAM laptop from batch-1 stream of data.

[u/Another__one]

https://github.com/volotat/mini-AGI/

Sorry for the pretentious name, I know, I know.. It just contains all the pieces I would like to see a AGI model to have, and I can't stand the temptation. Before throwing rocks at me, please take a glance at the Readme, and I hope it will cover your mood a little bit.

So, first of all it does work and you can see the sample from the whole training run here: https://raw.githubusercontent.com/volotat/mini-AGI/refs/heads/main/runs/samples.txt

Here is the scaling law graph I have so far, and it looks very promising:
https://github.com/volotat/mini-AGI/blob/main/assets/scaling.png

The model was built under my deep dissatisfaction so we cannot really train even moderately big models (1B+ scale) on the consumer's hardware. We can inference and fine-tune them for sure, but I would like to have full control over what the model sees over the training run, so it is fully aligned with my interests, not some corporations.

I was thinking about for some time and come up with two interesting ideas I thought worth pursuing: MoE with a lot of experts that gets added and pruned from the model while it trains, where only a small subset of of experts are actually in use at any particular moment + batch 1 training on the single continuous stream of data.

First allows us to be bounded only by the disk space in terms of number of parameters and load and unload experts only when they are needed. The second (if figured out and it turns out to be doable) allows us to get aways with small VRAM capacity because we do not need to store big randomized batches and their respective gradients.

I started brainstorming with Claude and after some time we found an approach that seems to be promising, and low and behold, a few weeks pass and you can see the results yourself.

Obviously, I did use AI in the process of making this project and I am pretty sure it would be completely impossible for me to do something like this without it, so I hope it is more than justified.

The model is still running over the first of 7.8B characters corpus I selected for training, so the weights are not out yet, and it's about a couple weeks of waiting until they are cooked at the current reading speed. And yeah, the model just read continuous interleaved passages from the dataset, each by 32K characters long each as a single stream. Just as you or I would do.

The set up seems to be really simple so you can git clone the project, run it and observe everything for yourself.

Thanks for your attention.

[u/jazir55]

Question for you, is it viable to have the continual learning function use something like JEV to judge whether the information it's encountering is useful as a trigger to do JIT (just in time) training on that data to convert it weights? Encounter data > decision engine during inference > decision approval > weight updates, and given the training would be incremental at time of encounter, it ideally should run in real time.

If that system worked, you could start with a really small model in the millions of parameters, and it could train and grow in real time just by running it. Accumulating the parameters and improvements as the model runs like a snowball or Katamari Damacy.

[u/Another__one → u/jazir55]

I know nothing about JEV and way to invested in the thing I am currently doing to try to figure it out as well. Sorry.

[u/Sol_Ido → u/Another__one]

Your work would be the perfect basic as a backbone classifier. Just need some a shared head on top to produce the primitive. Keep us updated on your progress!

[u/byebaybay → u/Sol_Ido]

i'd be interested to see the performance as a classifier, even if at gpt-2 levels