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license: apache-2.0
library_name: pytorch
tags: [dynamic-chunking, tokenization, hnet, byte-level, beetle-hnet, legacy-trainer]
---
# spec_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-spec-82m`](https://huggingface.co/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`](https://huggingface.co/datasets/AdaptiveChunking/hnet-chunking-probes) under `spec_B_s1/`.
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