UNTOKEN

Token compression for LLM prompts via a learned token selector.

UNTOKEN is a experimental architecture demonstrating adversarial autoencoder-based token importance scoring. Given N tokens, it returns a subsequence of ~0.3N tokens. The model shipped here (pacifio/untoken-v1) is trained at small scale as a proof of concept — the architecture is the contribution, not the weights.

Install

pip install untoken

Requires Python 3.10+ and PyTorch 2.1+. Works on CPU and GPU.

Context Window

The model processes up to 480 tokens per chunk (DistilBERT's 512-token limit minus special tokens). At ~20 tokens per average English sentence, that is roughly 20–24 sentences per chunk. Longer inputs are automatically split at sentence boundaries and compressed independently — no truncation occurs.

For best results, keep individual inputs under ~20 sentences. The model was trained at small scale and performs most reliably on short, self-contained passages.

Usage

from untoken import Untoken

ut = Untoken("pacifio/untoken-v2")

texts = [
    "The quick brown fox jumps over the lazy dog and then runs away into the forest.",
    "Scientists discovered a new species of deep-sea fish off the coast of Japan.",
    "The meeting was postponed due to a scheduling conflict with the board of directors.",
    "She completed the marathon in under four hours despite the difficult weather conditions.",
    "The server returned a 503 error after the deployment failed during the migration step.",
]

for text in texts:
    compressed, stats = ut.compress(text, ratio=0.4, return_stats=True)
    print(f"{text[:50]!r}...")
    print(f"  -> {compressed!r}")
    print(f"  -> {stats['original_tokens']} → {stats['compressed_tokens']} tokens ({stats['savings_pct']}% savings)\n")

"""
'The quick brown fox jumps over the lazy dog and th'...
  -> 'the quick brown fox jumps over dog'
  -> 19 → 9 tokens (52.6% savings)

'Scientists discovered a new species of deep-sea fi'...
  -> 'scientists discovered a new species of sea'
  -> 18 → 9 tokens (50.0% savings)

'The meeting was postponed due to a scheduling conf'...
  -> 'the meeting was postponed due scheduling'
  -> 17 → 8 tokens (52.9% savings)

'She completed the marathon in under four hours des'...
  -> 'she completed the marathon in hours'
  -> 16 → 8 tokens (50.0% savings)

'The server returned a 503 error after the deployme'...
  -> 'the server returned a 503 the'
  -> 18 → 9 tokens (50.0% savings)
"""

Note on v1 weights: The current model was trained on a small dataset and exhibits a known failure mode — it assigns high importance to frequent function words (determiners, auxiliaries) rather than content words. This is a training data scale issue, not an architectural one. The v1 checkpoint demonstrates that the full pipeline runs end-to-end. Improving selection quality requires more training data and longer adversarial fine-tuning.

Adjustable Ratio

compressed = ut.compress(text, ratio=0.5)  # keep 50%
compressed = ut.compress(text, ratio=0.2)  # keep 20%

No retraining required — ratio is applied at inference via top-k selection.

CLI

untoken --model pacifio/untoken-v1 --input prompt.txt --ratio 0.3

Long Documents

Inputs exceeding 480 tokens are automatically chunked at sentence boundaries.

with open("document.txt") as f:
    text = f.read()

compressed = ut.compress(text, ratio=0.3)

Evaluation (CNN/DailyMail, n=200, ratio=0.3)

Method	Cosine Sim	ROUGE-L	Compression Ratio
UNTOKEN	0.878	0.459	0.304
Random drop	0.723	0.429	0.303
Stopword removal	0.933	0.824	0.761

+15.5pp cosine similarity over random drop at equivalent compression ratio.

Architecture

The shipped artifact is a single ~300MB model:

• Encoder: DistilBERT-base-uncased (66M parameters)
• Importance head: Linear(768→256) → GELU → Dropout → Linear(256→1) → Sigmoid
• Selection: hard top-k over importance scores, preserving original token order

Training is a three-phase adversarial autoencoder:

1. Supervised warm-up — importance head trained on (original, compressed) pairs from MeetingBank
2. Adversarial fine-tuning — full generator trained against a discriminator on CNN/DailyMail
3. Hardening — Gumbel-softmax replaced with straight-through estimation to close the train/test gap

The reconstructor and discriminator are training-only and are not shipped.

See ARCHITECTURE.md for full details.

Performance

Primary metric — ROUGE-L:

Target ratio	UNTOKEN v2	LLMLingua-2	Random drop	Actual ratio (UNTOKEN / LLMLingua-2)
0.2	0.331	0.279	0.308	0.205 / 0.172
0.3	0.455	0.406	0.430	0.305 / 0.262
0.4	0.558	0.518	0.539	0.404 / 0.353
0.5	0.650	0.618	0.635	0.505 / 0.448

UNTOKEN v2 leads on ROUGE-L at every compression ratio tested. The gap over LLMLingua-2 is 4-5pp at low ratios, narrowing to 3pp at 0.5. UNTOKEN also consistently outperforms random drop, which is the baseline that requires zero learning — confirming the model is doing meaningful token selection and not just noise.

Model Size

Model	Parameters	Relative size
LLMLingua-2 (XLM-RoBERTa-large)	~560M	8.4× larger
LLMLingua-2 (BERT-base-multilingual)	~179M	2.7× larger
UNTOKEN v2	66.56M	1×

Training Data

v2 was trained on 7 datasets across diverse domains:

Dataset	Domain	Supervision type	~Records
MeetingBank	Meeting transcripts	Paired (summary)	20K
CNN/DailyMail	News articles	Unlabeled	300K
XSum	BBC news	Paired (summary)	200K
DialogSum	Conversation	Paired (summary)	14K
BillSum	Legislation	Paired (summary)	23K
BookSum	Long-form books	Paired (summary)	12K
GSM8K	Math reasoning	Unlabeled (discriminator real pool)	8K

See report.md for more details.

Model

pacifio/untoken-v1 — trained on MeetingBank + CNN/DailyMail at small scale. pacifio/untoken-v2 — more diverse dataset

License

MIT