cruxtok / README.md

CruxTok (T1) and NCM baseline tokenizers trained on XSum

16772ec verified about 12 hours ago

3.1 kB

license: mit
language:
  - en
tags:
  - tokenizer
  - vector-quantization
  - residual-vq
datasets:
  - EdinburghNLP/xsum

CruxTok tokenizers (XSum)

Residual VQ text tokenizers from the paper What Does the First Level of a Residual Text Tokenizer Know? Learned-Query Pooling Puts the Gist in Level 1 (arXiv link to follow). Code: github.com/Zilch123/CruxTok.

CruxTok (T1) replaces the fixed-window pooling of Next-Concept Modeling (NCM) with learned-query pooling. Against an NCM baseline with the same parameter count, its first level (4 codes) recovers much more of a sentence.

Files

Folder	Model	Params	Seed, precision	Text accuracy, k=1 / k=3 / full
`t1_seed42`	CruxTok (T1)	73.2M	42, bf16	23.5 / 60.1 / 98.8
`t1_seed2`	CruxTok (T1)	73.2M	2, bf16	24.4 / 60.7 / 98.8
`t0cap_seed42`	NCM, same budget (T0-cap)	60.6M	42, fp16	23.5 / 47.1 / 95.8
`t0cap_seed2`	NCM, same budget (T0-cap)	60.6M	2, bf16	23.0 / 46.5 / 95.7
`t0cap_matched`	NCM with 5 encoder and 3 decoder blocks	73.1M	42, bf16	19.3 / 45.6 / 97.8

Each folder holds tokenizer.pt (weights and config), config.yaml and eval.jsonl (held-out reconstruction every 5,000 updates). Parameter counts include codebooks. "k" means levels 1..k together: k=1 is 4 codes, k=3 is 20, and full is all 9 levels over a 48-token window (GPT-2 tokenizer).

All models were trained on XSum training summaries for 30,000 updates at batch 256, about 16x below NCM's tokenizer budget.

Usage

git clone --recurse-submodules https://github.com/Zilch123/CruxTok && cd CruxTok && pip install -e .

from cruxtok.model.cruxtok import CruxTok

tok = CruxTok.from_pretrained("TimothDevA/cruxtok", filename="t1_seed42/tokenizer.pt")
codes = tok.encode("West Ham striker Andy Carroll is facing up to six weeks out of action.")
print(tok.decode(codes, upto_level=1))   # from the first 4 codes only
print(tok.decode(codes))                 # all levels

The NCM baselines load through the repository's NCM wrapper:

from huggingface_hub import snapshot_download
from cruxtok.eval.semantic import load_run

path = snapshot_download("TimothDevA/cruxtok", allow_patterns="t0cap_matched/*")
tokenizer, cfg = load_run(f"{path}/t0cap_matched", "cpu")

Results at level 1

	Embedding cosine to original	LLM judge, CruxTok win rate
T0-cap	0.359	0.700 [0.678, 0.722]
T0-cap, matched	0.342	0.740 (seed 42), 0.772 (seed 2)
CruxTok (seeds 42 / 2)	0.449 / 0.455

On 1,000 XSum test summaries. The judge (GLM-5.3, validated on STS-B at Spearman 0.90) compares level-1 reconstructions in both orders. CruxTok does not make similar sentences get similar codes: on STS-B at k=3, code similarity reaches Spearman 0.28 for the matched baseline against 0.02-0.03 for CruxTok.

Attribution and license

MIT. Built on Next-Concept Modeling (Kumbong et al., arXiv:2610.14162, MIT).