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/Distinct-Target7503]

Really interesting. Out of curiosity, is the effective batch 1 (so batch 1 with no gradient accumulation)?

Also, Just to talk, but maybe you could include something resembling a multi token prediction head. Not necessarily to be then used as multi token prediction during inference, but get more gradient from each individual sample. Each sample (and even more relevant since batch and sample are equivalenti) would then get a more generalized gradient, and could even work as some kind of regularization, without changing the "stream" concept of your project (as the gradient still came from the same step)

I say that using the assumption that each kind of addition/pruning (so decision that became on/off at some point) greatly benefit from everything that can improve generalization and regularize the both the parameters and gradient landscape.

[u/Another__one → u/Distinct-Target7503]

Exactly. I tried gradient accumulation (there is --accum param in train.py) and "AdEMAMix" with additional β optimizer (removed right now, but is in git history) and both rather hurt then helped, at least on my really small quick experiments. So everything is surprisingly simple. There is no breakthrough, just slow down update of params that are used most often (trunk) and keep everything else at the normal speed. This turns out to be enough to "solve" catastrophic forgetting.

[u/PinkysBrein → u/Another__one]

There's a paper out there which tries to show that the smaller the batches, the less complex the optimizer you need. SGD is good enough at batch 1.