terminal output

Utah teapot 🫖→vibecamp SOON (@SkyeSharkie ✓) i guess fable wanted to take a break, it output this fake api policy violation warning and stopped doing what it was doing, lol, this is actually from its text output and the conversation was able to be continued just fine xD [Embedded image: stylized amber/yellow terminal-font text on black background, cropped, reading:] "...respond to this request, which ). Please double press esc to e...th a different task." 11:32 PM · Jun 11, 2026 · 1,417 Views

● Major finding already: the log shows "VRAM after setup: 1411MB" — so params + optimizer + buffers are only ~1.4 GB. The OOM at 22.96 GB is almost entirely train-tick activations / the three_block —autograd graph (~21 GB). The static base is not the problem; the activation memory is. (My helper

| 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]