| --- |
| license: apache-2.0 |
| library_name: pytorch |
| tags: [dynamic-chunking, tokenization, hnet, byte-level, beetle-hnet] |
| --- |
| |
| # ali_ali_s1 |
|
|
| A byte-level H-Net run from [beetle-hnet](https://github.com/suchirsalhan/beetle-hnet) |
| (`architecture=ali`, `objective=ratio` |
| `schedule=None`, `dechunk=ema`, seed 1, |
| 6000 steps x 32 x 1024 bytes, langs ['en', 'tr', 'fi']). |
|
|
| ## Layout (Beetle-style) |
|
|
| - **`step-N` branches**, one per kept checkpoint on a phase-aware log2 schedule (0, 1, 2, 4, 6, |
| 8, 16, ..., 2^k from every objective-phase start, plus the final step). Each holds |
| `model.safetensors`, `config.json`, `training_config.yaml` and the full resumable trainer |
| state `training_state/checkpoint.pt` (for inventory arms this includes the inventory). |
| - **`main`**: the latest step's weights and config, plus `log.jsonl`, `final.json` and the |
| FLORES+ boundary probes `probe/` once the run has finished. |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import torch |
| p = hf_hub_download("AdaptiveChunking/ali_ali_s1", "training_state/checkpoint.pt", revision="step-1024") |
| state = torch.load(p, map_location="cpu", weights_only=False) |
| ``` |
|
|
| `model.safetensors` stores each tied tensor once; `config.json["tied_weights"]` maps the |
| dropped names to the stored ones. |
|
|
