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PPT Corpus: control_nca

Raw stage-1 pre-pretraining corpus from the ppt research framework, exported for information-theoretic analysis (e.g. m-local entropy) independent of this repo's training pipeline.

IMPORTANT: token IDs are NOT standard BPE tokens

Same NCA patch IDs as nca, with token order scrambled per chunk (see below).

These input_ids are not decodable with the Pythia (or any other) tokenizer. They are integers in [0, 10002) with regimen-specific meaning (see above) — treat this dataset as a corpus of symbol/event sequences, not text.

Corpus stats

Field Value
Vocabulary size 10002
Chunk length (seq_len) 2048
Number of chunks 160,000
Total tokens 327,680,000
Generation seed 42

Each row's input_ids is a fixed-length list of 2048 integers — the exact chunk granularity fed to the model during stage-1 training (formed by concatenating generated sequences and splitting into non-overlapping 2048-token blocks).

Control regimen: token order within each 2048-token chunk is randomly permuted (torch.manual_seed(0) + per-row torch.randperm), preserving the exact token multiset/vocabulary distribution of nca while destroying sequential structure. This snapshot is a fixed, seeded reproduction generated at export time — for control_nca specifically, note that the live training path (ScrambledIterableDataset in src/ppt/data/control.py) draws its permutation unseeded per iteration, so this uploaded corpus is representative of the control regimen's statistics but is not a byte-identical capture of any particular training run.

Generation config

{
  "grid_size": 12,
  "patch_size": 2,
  "num_colors": 10,
  "d_state": 10,
  "num_rules": 5000,
  "buffer_size": 256,
  "filter_threshold": 0.5,
  "dT": 5,
  "rollout_steps": 10,
  "jax_seed": 42,
  "seq_len": 2048
}

Usage

from datasets import load_dataset

ds = load_dataset("sashaboguraev/ppt-control_nca-corpus")
example = ds["train"][0]["input_ids"]  # list[int], length 2048
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