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?   submitted by   /u/returnity [link]   [comments]