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

# pilot_B_s1

Byte-level H-Net run from [beetle-hnet](https://github.com/suchirsalhan/beetle-hnet), trained
with the legacy trainer (`legacy/tier1/train.py`). Split out of the bundled repo
[`AdaptiveChunking/hnet-chunking-pilots`](https://huggingface.co/AdaptiveChunking/hnet-chunking-pilots) into the Beetle per-run layout.

- **`step-N` branches**: 1 retained checkpoints (6000),
  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`](https://huggingface.co/datasets/AdaptiveChunking/hnet-chunking-probes) under `pilot_B_s1/`.