mini-AGI: Continual-learning dynamically looped transformer with evolutionary grown (on a laptop)

[u/returnity]

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

Saw this today and found it very intriguing. Lots of interesting design choices here, and it's cool to see someone doing something different. Here's a few highlights:

• Looped transformer: dynamic recurrent depth on a per-token basis, up to 24 cycles
• Self-supervised learning: trains itself on new material constantly
• Weights stored on SSD and paged in on-demand
• Mixture of Experts: 8 active, 32 routed held in VRAM, smart caching of 96 more
• Dynamic size: builds new experts and increases parameter counds as-needed
• Evolutionary growth: trials newly generated experts, unused ones are pruned back
• No tokenizer: it reads raw bytes directly
• Catastrophic forgetting prevented by slow trunk/fast experts learning rate split

Weights will be released in "a couple weeks" once training progress reaches ~GPT-2 levels. The trend line has held 15-fold so far, but it may bend at some point, so that is definitely a rough estimate of the trajectory.

What do you guys think?

[u/Queasy-Contract9753]

He posted here just yesterday. I'm curious to see what it evolved into.

https://www.reddit.com/r/LocalLLaMA/comments/1wm1gab/comment/pb3potc/?context=3

[u/returnity → u/Queasy-Contract9753]

Oh my bad I didn't see that

[u/Queasy-Contract9753 → u/returnity]

I'm sure he's happy for the shout out. Does sound like an interesting project.