---
license: cc-by-4.0
tags: [go, baduk, weiqi, katago, mcts, distillation]
---
# vibego-b6c96nbt-pat

561-MFLOP frontier point (0.9M params, ~8.2 CPU-ms) with the 3x3 dihedral pattern table (~+200 Elo at this size, ~0 FLOPs). Weights-only.

## Format / usage

PyTorch checkpoint: `{'model': state_dict, 'model_config': dict, 'spatial_subset': [...],
'global_subset': [...], 'step': int}` (full checkpoints also carry `optimizer`). Inputs are a
14-channel subset of KataGo v7 spatial features + 2 global features; the engine, training
pipeline, and evaluation harness will be released at https://github.com/sanderland/nanogo (the study writeup lives there
under `experiments/WRITEUP.md`). Strength numbers are judge scoreLead / win-rate Elo from
paired color-reversed-opening matches at 48 visits/move with a kata1-b18 judge.

Distilled from the public `kata1-b18c384nbt` net over katagoarchive.org positions — credit to
lightvector and the KataGo distributed-training contributors.