README.md

metadata

license: apache-2.0
library_name: pytorch
tags:
  - dynamic-chunking
  - tokenization
  - hnet
  - byte-level
  - beetle-hnet
  - legacy-trainer

exp32_onechunk_s1

Byte-level H-Net run from beetle-hnet, trained with the legacy trainer (legacy/tier1/train.py). Split out of the bundled repo AdaptiveChunking/hnet-chunking-spec-82m into the Beetle per-run layout.

• step-N branches: 21 retained checkpoints (0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 1000, 2000 ...), each with model.safetensors and config.json (model_config, training config and the tied_weights map: restore with sd[dst] = sd[src] for every entry).
• main: the latest weights plus final.json and log.jsonl.
• The legacy trainer kept no optimiser state, so there is no training_state/ here. FLORES+ boundary probes for this run are in the dataset AdaptiveChunking/hnet-chunking-probes under exp32_onechunk_s1/.