tokenizer string | vocab_size int64 | num_tokens int64 | source string | split string | dtype string | gen_params dict |
|---|---|---|---|---|---|---|
nca_patch | 10,002 | 164,160,000 | nca | train | uint16 | {
"variant": "paper",
"filter_steps": 10,
"grid_size": 12,
"num_colors": 10,
"patch_size": 2,
"seq_len": 1024,
"tokens_per_grid": 38,
"grids_per_trajectory": 27,
"tokens_per_trajectory": 1026,
"start_token": 10000,
"end_token": 10001,
"gzip_threshold": 0.5,
"gzip_upper_bound": 1,
"num_rules"... |
nca-paper-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:
from huggingface_hub import hf_hub_download
import numpy as np
path = hf_hub_download(repo_id="alexkstern/nca-paper-seq_len_1024-164M", filename="train.bin", repo_type="dataset")
tokens = np.memmap(path, dtype="uint16", mode="r")
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