parameter counts

| Component | Trainable params | Source |al) Counted dire— ───────────────────────────────────────── s | params | | Sharded macrocolumn cortex (425,393 | 184,354,125 | banner param_count() over | | | macrocolumns) | | requires_grad | Counted dire— Component | Trainable ─────────────────────────────────────────s • ├ — — | 184,354,125 | ba ner param_count() over────────────| | Sharded macrocolumn cortex (425,393 | | requires_gradount() over | | | Backbone (RWKV nodes, MONET keys, heads, | 37,302,731 | optimizer exp_avg entries (one | | | BG/motor/hippo/grid/etc.) | | moment per optimized param) | | Total trainable | ≈ 221.7 M | The macrocolumn cortex is 88% of the trainable weights — as expected, it's the whole point of the sharded run (it's also why N=425k doesn't fit one 24 GB card and needs the 2-way split). N te on the bigger number you'd see h̶u̶ external-drive/ 5 shells • buffers: EMA target encoders, CSR/edge-tructure tensors, divnorm running-σ, fast-plastic • local_learning traces (13.6 M), surprise memory (4.8 M), etc. Those don't get gradients, so they're not in the "trainable" count. • - The macrocolumn block's own per-shard optim[izers] are built at runtime and aren't in thiso checkpoint's exp_avg, which is why I took its trainable count from the startup banner's param_count() stead — the two method cover disjoint halves, so they sum cleanly. So: ~222 M trainable, ~184 M of it in the GPU-sharded cortex, with another ~73 M of non-trained [buffers/state cut off at bottom of visible screen]