---
pretty_name: nca-paper-share10-seq_len_1024-164M
tags:
- pretokenized
- synthetic
- cellular-automata
- nca
size_categories:
- 100M<n<1B
---

# nca-paper-share10-seq_len_1024-164M

Procedurally generated **Neural Cellular Automata** trajectories (Lee et al. 2026), as flat `uint16` token-id `.bin` files. Random NCA rules are rolled out on a 12×12 grid of 10 cell states and tokenized by 2×2 patches (base-10); only high-complexity rules survive a gzip-ratio filter (kept iff in (0.5, 1.0)). Token ids: 10,000 patch ids plus two grid delimiters (start=10000, end=10001); vocab = 10,002.

**Configuration**

| param | value |
| --- | --- |
| grid | 12×12 |
| colors (states) | 10 |
| patch | 2×2 |
| seq_len (target) | 1024 |
| tokens / grid | 38 |
| grids / trajectory | 27 |
| tokens / trajectory | 1026 |
| gzip band | (0.5, 1.0) |

| file | split | tokens |
| --- | --- | --- |
| `train.bin` | train | 164,160,000 |
| `val.bin` | val | 10,000,422 |

The bin is a concatenation of fixed-length **1026-token trajectory records** (`num_tokens` is a whole multiple of it): load via `tokens.reshape(-1, 1026)`, then pack one or more trajectories per row as needed. The delimiter ids (10000, 10001) mark grid boundaries and are masked in the loss. `train` (seed 0) and `val` (a disjoint seed) are independent streams of the same generator; token count = `filesize / 2`; the metas carry the full config.

**The complexity filter is per-rule.** The gzip band (0.5, 1.0) is applied to each *rule* once, scored on a single random init (a 10-grid rollout). The sims generated per kept rule use fresh inits and are not re-scored, so — because NCA rules can be init-sensitive (multiple basins) — a tiny fraction (~0.01% of trajectories) collapse to a degenerate fixed point (e.g. a uniform grid), and such collapses can repeat exactly across sims. These are low-complexity and harmless; it is inherent to the per-rule filter, not a generation error.

**Load a bin** with the standard Hugging Face downloader:

```python
from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(repo_id="alexkstern/nca-paper-share10-seq_len_1024-164M", filename="train.bin", repo_type="dataset")
tokens = np.memmap(path, dtype="uint16", mode="r")
```