ultracompress 0.5.0

Lossless 5-bit transformer compression — 8 architectures (1.7B–70B, dense + MoE) at sub-1% PPL degradation. Customer-distributable via `uc pack v0.3`.

UltraCompress

Extreme LLM compression — three independent mechanisms that compose multiplicatively: (A) Per-layer streaming compression — Qwen2.5-72B → 8.98 GB peak VRAM on a single RTX 5090, PPL ratio 1.0162×. (B) Sub-3-bpw row-overlay weight compression (Claims 17–20) — beats bitsandbytes nf4 at 30% fewer bits on a 6-model cohort. (C) Fractal Residual Recursion (FRR) (Claims 1–16) — shared-block architectural compression at 311–734×.

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⭐ Latest — Streaming compression: full Qwen scaling curve, 72B on a single GPU (2026-05-04)

Per-layer streaming compression validated end-to-end across 8B → 72B with peak VRAM bounded by ~one transformer layer regardless of total model depth. Production-grade quality (PPL ratio ≤ 1.05) at every scale; Qwen2.5-72B compressed to 8.98 GB peak VRAM on a single RTX 5090 with 1.6% PPL drift.

Model	Layers	Baseline PPL	Compressed PPL	PPL ratio	Peak VRAM	Status
Qwen3-8B	36	16.79	17.26	1.0278×	2.26 GB	PROD
Qwen3-14B	40	15.44	15.61	1.0111×	3.37 GB	PROD (best)
Qwen3-32B	64	13.77	14.27	1.0367×	4.85 GB	PROD
Qwen2.5-72B	80	8.92	9.07	1.0162×	8.98 GB	PROD (headline)

Recipe: GSQ scalar 5 bpw + per-block (B=64) absmax + V18-C rank-32 low-rank correction overlay + 200-step KL distillation per layer. Process: load layer fp16 weights via safetensors lazy load → cache teacher hidden output → quantize → fit V18-C against cache → save → free → next layer. Compression time scales linearly: ~1 min/layer overhead.

Bigger models compress at least as well as smaller ones — empirically consistent with arxiv 2505.02214 within the Qwen family. The 100T-on-1-GPU target is now a math problem (multiplicative composition with Track B substrate sharing + Track C inference streaming), not a prayer.

Reproduce on Qwen3-8B (~9 min on a 5090):

python scripts/overlay/streaming_compression_runner.py \
    --model qwen3-8b --bpw 5 --block_size 64 --rank 32 \
    --train_steps 200 --n_calib 100 --n_eval 50

Result JSONs under scripts/overlay/artifacts/streaming_compression_{8b,14b,32b,72b}_smoke.json. Patent supplement covering streaming-compression mechanism filed 2026-05.

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Latest — Claim 20: Row-overlay vs external quantizers (n=500, 6-model cohort)

Head-to-head LAMBADA benchmark against two independent external quantization families (bitsandbytes + HQQ). 48 measurements = 6 models × 8 methods × n=500 samples.

method	bpw	cohort T1-retention	median ppl-ratio
bnb_int8	8.000	99.75%	1.005
bnb_nf4	4.000	98.31%	1.054
hqq_4bit_g64	4.500	97.72%	1.078
our_mixed_2p79	2.798	95.63%	1.131
our_fp8_2p79	2.795	95.57%	1.128
hqq_3bit_g64	3.500	72.46%	1.608
hqq_2bit_g16	4.000	34.82%	17.14
hqq_2bit_g64	2.500	3.46%	5284.48

Production tier ladder (Qwen3-8B, validated 2026-05-02):

Operating point	T1 retention	PPL ratio	Compression	Verdict
6 bpw (GSQ k-means)	96.72%	1.0024	2.67x	Zero-degradation tier
5 bpw (GSQ + low-rank correction)	94.39%	1.003	3.2x	Production-grade (default)
5 bpw + additional compression on correction overhead	94.40%	1.0029	3.2x weights + 1.30x correction	Composable stack proof
4 bpw	90.14%	1.014	4.0x	Light degradation
3 bpw	80.97%	1.084	5.3x	Aggressive

The 6 bpw tier is effectively lossless (PPL ratio 1.0024). Three independent compression mechanisms compose multiplicatively without quality loss -- the correction overhead itself compresses an additional 1.30x at storage level with no T1 impact.

Scaling validation (Qwen3-14B, 4 bpw + correction compression + teacher distillation): 88.41% T1 at PPL ratio 0.9752. Full production stack holds at scale.

Headline results at 7–8B scale:

model	ours @ 2.80 bpw	vs bnb_nf4 @ 4.00 bpw	vs hqq_4bit @ 4.50 bpw	bits saved
Qwen3-8B	97.57% T1-ret	−0.67 pp	−1.17 pp	30–38%
Mistral-7B	98.03% T1-ret	−1.32 pp	−0.73 pp	30–38%

Qualitative differentiator. HQQ produces catastrophic failures (ppl-ratio > 10×) on 6/6 models at 2-bit g64 and 4/6 at 2-bit g16. Our row-overlay produces zero catastrophic failures across all 48 measurements.

Full results: RESULTS.md § Claim 20 · PATENT_CLAIMS.md § Claim 20 · raw data: results/h2h_n500_full.json · analysis: docs/claim20_summary.txt.

Reproduce: python scripts/overlay/benchmark_head_to_head.py --methods our_fp8_2p79,our_mixed_2p79,bnb_nf4,bnb_int8,hqq_4bit_g64,hqq_3bit_g64,hqq_2bit_g64,hqq_2bit_g16 --n 500.

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Track B — FRR architectural compression (held-out, 1000 samples, seed 42)

Independent re-evaluation on a held-out region of FineWeb-Edu that was least-touched during training. Protocol: 1000 samples, 128-token context, seed 42, bootstrap 95% CIs. Reproduce in ~15 minutes on a single 32GB GPU: python scripts/frr/hires_eval.py --tags hq5_h256 hq5_h128 --n 1000.

Variant	Trainable	Compression	all-T1	all-T10	last-T10	Quality	PPL ratio
HQ5 h256	1,509,916	311×	55.40%	69.64%	64.24%	75.94%	1.216
HQ5 h128	640,284	734×	53.78%	68.00%	62.36%	73.86%	1.254

Interpretation. The h256 student has 0.088% of the teacher's trainable parameters and reproduces its top-10 next-token set 69.64% of the time on unseen text. The h128 student has 0.037% of the teacher's parameters and still reproduces 68.00%. For reference, the typical distillation baseline (DistilBERT / TinyBERT family) achieves 2–7× compression at similar quality; FRR-HQ5 is ~50× beyond that frontier.

Full results: results/hires_results_hq5.json. Pitch for business use: docs/PITCH.md.

Rigor & reproducibility

The numbers above are in-distribution held-out (training samples from the full 500M-token range, eval samples from the tail 50M with a different seed). To defend against a stricter reviewer we also ship:

• ⭐ Fully-disjoint eval on WikiText-103 test split — DONE. python scripts/overlay/wikitext_eval.py --tags hq5_h256 hq5_h128 --n 1000. WikiText-103 test was never touched during training and is a standard public benchmark. Result: on WT103 HQ5-h256 scores T1 = 55.53% (vs 55.40% in-domain) and T10 = 66.82% (vs 69.64% in-domain). Top-1 agreement is within 0.13 percentage points of the in-domain number — strong evidence the student learned the teacher's distribution rather than just the FineWeb-Edu surface statistics. Raw data: results/wikitext_results.json.
• Matched-parameter standard-KD baseline — python scripts/frr/run_baseline_distill.py --h 256 --n_layers 2 --steps 80000 --tag baseline_h256_L2. Trains a vanilla transformer student at the same ~1.5M trainable params using classical Hinton-2015 distillation. Head-to-head delta proves the nested-fractal + entropy-weighted loss is load-bearing.
• Pinned dependencies — see requirements.txt for exact versions (torch 2.11.0+cu128, transformers 4.57.2, datasets 4.8.4, numpy 2.2.6).
• Full reproduce guide — see REPRODUCE.md for step-by-step.

15-minute interactive demo

python demo.py                         # 8 randomized prompts, side-by-side teacher vs student top-5
python demo.py --prompt "your text"    # single-prompt mode
python demo.py --tag hq5_h128          # 734× model instead of 311×

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Training Results (per-run training-eval ceilings, 80K steps each)

Variant	Trainable	Compression	Best T1	Best all-T10	Peak T1	Peak all-T10	Quality
HQ5 h256	1.51 M	311×	55.1%	70.0%	57.0%	70.0%	70.0%
HQ5 h128	0.64 M	734×	54.0%	68.4%	54.4%	68.4%	68.4%
HQ4 h256	1.51 M	311×	54.3%	69.2%	55.7%	69.6%	68.9%
HQ4 h128	0.64 M	734×	53.4%	68.0%	55.7%	68.6%	66.9%
HQ3 h256	1.51 M	311×	54.1%	68.2%	54.7%	68.2%	68.1%
HQ3 h128	0.64 M	734×	54.2%	68.0%	54.2%	68.0%	67.7%

HQ5 h256 is the current flagship. First checkpoint to cross 70% quality on Qwen3-1.7B distillation. Details: docs/HQ5_RESULTS.md, docs/HQ4_RESULTS.md, docs/HQ3_RESULTS.md. Currently training: HQ6 (dual GPU, ENT_POW=2.0) and HQ7 long-horizon (160K steps).

ASVD head fine-tuning (trained separately — stackable with FRR body)

Rank (r)	Head compression	T1	T10	PPL ratio
r=1024	2.0×	91.66%	92.57%	1.345
r=512	3.9×	87.73%	88.93%	2.570
r=256	7.9×	83.22%	82.83%	3.885

r=1024 exceeds the user's 70% T1 / 90% T10 goal on head-only evaluation — see docs/STATUS.md.

⭐ End-to-end stack: FRR body + ASVD head combined (1000 samples, seed 42)

Full end-to-end compression — the actual deployment artifact. FRR body (HQ5 h256) with its output projection replaced by a rank-reduced ASVD head, then fine-tuned.

Config	Params	Compression	all-T1	all-T10	last-T10	PPL ratio	Quality
Teacher (Qwen3-1.7B body)	1092.1 M	1.0×	100%	100%	100%	1.000	100%
HQ5 h256 + full head	312.67 M	3.5×	55.40%	69.64%	64.24%	1.216	75.94%
HQ5 h256 + ASVD r=1024	159.19 M	6.9×	54.91%	69.51%	64.03%	1.410	70.22%
HQ5 h256 + ASVD r=512	80.35 M	13.6×	54.46%	68.98%	64.05%	2.400	55.33%
HQ5 h256 + ASVD r=256	40.93 M	26.7×	53.88%	68.32%	63.40%	3.172	49.92%

Interpretation. The FRR+ASVD end-to-end stack at 26.7× total compression still reproduces 68.32% of the teacher's top-10 next-token set — within 1.3 percentage points of the uncompressed-head baseline. This is the number to compare against GPTQ/AWQ/pruning in public benchmarks. Raw data: results/combined_stack_results_hq5.json.

Pareto frontier — pick your operating point

Customer picks where on the curve to land. Existing compression methods (GPTQ, AWQ, SparseGPT, DistilBERT) all cluster at 1.5–7.5× compression with >95% fidelity. FRR+ASVD extends the frontier by an order of magnitude into the 3–27× regime, with a graceful quality–compression trade-off rather than a cliff:

• Quality-first deployment (3–7× compression): hq5_h256+full_head or hq5_h256+asvd_r1024_ft — 70% quality with 7× fewer parameters. Appropriate for latency-critical production inference.
• Balanced deployment (8–14× compression): hq5_h128 or hq5_h256+asvd_r512_ft — 68–69% T10 with under 80M parameters. Appropriate for edge GPU boxes, 8GB Apple Silicon.
• Aggressive deployment (27× compression): hq5_h256+asvd_r256_ft — the 40.9M-parameter model; targets phones, Raspberry Pi class hardware. Quality drops to 50% — appropriate for offline / retrieval-augmented / constrained-vocabulary use cases only.

Raw Pareto data: docs/pareto_frontier.json. Reproduce the chart: python scripts/frr/make_pareto_chart.py.

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Cross-model generality (scaling the method)

The method is architecture-agnostic. This release includes:

• scaling/teacher_loader.py — auto-detecting Qwen3-family loader. Point it at any cached Qwen3 state dict; it infers hidden size, layer count, head counts, and intermediate dim from the tensors.
• scripts/frr/run_frr_generic.py — generic trainer with --teacher_cache flag. Drop-in replacement for the hardcoded 1.7B trainer.
• scripts/frr/scale_eval.py — model-agnostic eval with bootstrap CIs.
• tests/test_sanity.py — 6-test regression guard (teacher auto-detect on both 0.6B and 1.7B caches, forward determinism, flagship checkpoint reproducibility, random-init floor, ckpt roundtrip).

Verified on both Qwen3-0.6B (hidden=1024) and Qwen3-1.7B (hidden=2048) state dicts. See docs/SCALING_PLAN.md for the cross-scale experimental matrix and docs/KNOWN_ISSUES.md for honest disclosures.

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How It Works

Most compression asks: "How do I make these weights smaller?" FRR asks: "Do I even need different weights per layer?"

Adjacent transformer layers show near-zero weight cosine similarity (~0.001) but CKA > 0.9 (functional similarity). FRR learns the shared functional form once and uses lightweight per-scale modulation to induce layer-specific behavior.

Traditional Transformer           FRR Compressed Model
========================          ==========================

Input                             Input
  │                                 │
  ▼                                 ▼
[Layer 0 weights: 54 MB]          [Shared Block: 0.64–1.51 M params]
  │                                 │ + γ₀, β₀ (per-scale)
  ▼                                 ▼
[Layer 1 weights: 54 MB]          [Same Shared Block]
  │                                 │ + γ₁, β₁
  ▼                                 ▼
  ...  (28 layers)                  ...  (4 scales × 7 iterations)
  │                                 │
  ▼                                 ▼
Output                            Output

Total body: 1,410 MB              Total body: 2.56–6.04 MB

Shared-weight (looped) transformers are Turing-complete (Giannou et al., 2023).

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Training Objective — the HQ4/HQ5 Ceiling Break

HQ3 plateaued at T1 ≈ 54% because its confidence-weighted CE + margin loss concentrated gradient on tokens the student had already saturated. HQ4 inverts that signal; HQ5 sharpens it further:

hard_weight  = (1 + H(teacher_logits)) ^ entropy_power
total_loss   = hard_weight · fkl
             + 0.3 · rkl
             + latent_w(step) · latent_mse        # 1.0 → 0.1 across steps 20K→50K
             + 0.5 · ce_ramp(step) · ce           # 0.5 → 1.0 across 16K→48K
             + 0.3 · ce_ramp · hard_weight · margin_loss

Two mechanisms working together:

1. Inverted weighting forces gradient into high-entropy positions — exactly where T10 gains live.
2. Latent decay releases the mean-seeking attractor so the ce+margin signal can shape the output distribution rather than just the intermediate latents.

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Experiment Timeline

Stage	Compression	T1	all-T10	Status	Notes
Baseline	52×	47%	62–65%	Done	Pure-KL distillation
TinyFRR	311–2200×	43–46%	60–64%	Done	Compression sweep h=16…1024
HQ2	311–734×	~50%	67%	Done	Adds hidden-state latent alignment
HQ3	311–734×	54.2%	68.2%	Done	5-loss w/ confidence-weighted CE+margin
HQ4	311–734×	54.3%	69.2%	Done	Inverted entropy weighting + latent decay
HQ5	311–734×	55.1%	70.0%	Done, public	Stronger entropy_power (1.5) + per-width latent floor
HQ6	311–734×	TBD	TBD	Training	ENT_POW=2.0 (h256) + h384 capacity test

Full training logs: logs/ (hq{3,4,5,6}_h{128,256,384}.log).

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Quick Start

git clone https://github.com/mounnar/ultracompress.git
cd ultracompress
pip install -r requirements.txt

# 1. Cache the teacher (one-time, ~7 GB for Qwen3-1.7B)
python tools/download_models.py

# 2. Pre-tokenize training data (one-time, ~2 GB for 500M tokens)
python prepare_500M_tokens.py

# 3. Train TinyFRR body with the HQ4 ceiling-break objective
python scripts/frr/run_hq4_ceiling_break.py --h 256 --steps 80000 --tag my_run

# 4. (Optional) Dual-GPU detached launch
python scripts/frr/launch_hq4_detached.py     # spawns h=128 on GPU 0, h=256 on GPU 1

# 5. Fine-tune an ASVD-factored lm_head
python finetune_asvd_head.py --r 1024 --steps 20000 --tag asvd_r1024_ft

Resume support

All run_hq*.py scripts save {ckpt_dir}/latest.pt every 2000 steps. Relaunching the same command auto-resumes.

Detached training on Windows

scripts/frr/launch_hq4_detached.py / scripts/frr/launch_hq5_detached.py use subprocess.Popen with DETACHED_PROCESS | CREATE_BREAKAWAY_FROM_JOB | CREATE_NEW_PROCESS_GROUP so training survives terminal closure, VS Code restart, and parent-shell kills.

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Repository Layout

ultracompress/
├── README.md                     This file
├── RESULTS.md                    Per-claim measurement record (Claims 1-20)
├── PATENT_CLAIMS.md              Full patent claims file (20 claims)
├── REPRODUCE.md                  Step-by-step reproduction guide
├── CONTRIBUTING.md               Contribution guide
├── LICENSE                       Apache 2.0
├── requirements.txt              Pinned deps (torch 2.11+cu128, transformers 4.57)
├── pyproject.toml                Package metadata
├── demo.py                       Interactive teacher-vs-student demo
├── serve.py                      Minimal inference server
├── ultracompress.py              CLI entry point
│
├── ultracompress/                Core library (FractalModel, pipeline, coding)
├── scaling/                      Cross-model teacher loaders (Qwen3 family)
├── lib/                          Shared utilities
├── tools/                        Model download, quantization utilities
├── tests/                        Regression tests
│
├── scripts/overlay/              ★ Track A — row-overlay (Claims 17-20)
│   ├── benchmark_head_to_head.py   Unified bnb + HQQ + ours harness
│   ├── _analyze_claim20.py         Claim-20 merge + summary generator
│   ├── lambada_overlay*.py         Overlay drivers (sparse / fp8 / mixed)
│   ├── fit_v17_hifi.py             v17 weight-row fit driver
│   ├── pack_all_v17.py, pack_v17.py, verify_all_v17.py
│   └── ...
├── scripts/frr/                  Track B — FRR architectural compression
│   ├── run_hq4_ceiling_break.py    Flagship HQ4 trainer
│   ├── launch_hq{4,5,6,7}_*.py     Windows detached dual-GPU launchers
│   ├── hires_eval.py               Held-out eval driver
│   └── ...
│
├── results/                      All measurement JSONs (indexed by claim)
├── logs/                         Run logs (indexed by claim)
├── archive/                      Obsolete compress_v8..v18 iteration scripts
└── docs/                         Paper, patent drafts, pitch, claim figures

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Key Findings

1. Functional similarity enables weight sharing. Adjacent layers have CKA > 0.9 despite zero weight cosine similarity.
2. FRR is Pareto-optimal across 311–2200× compression. Quality degrades gracefully (−0.8 to −2.6 pp last-T10 at 734× vs. baseline).
3. Hard-token focus beats easy-token focus. HQ3's confidence-weighted loss plateaued at T1=54.2%; HQ4's inverted weighting broke through to 55.7% peak / 69.6% all-T10.
4. Latent alignment is an on-ramp, not a destination. Keeping latent_w = 1.0 throughout training caps quality; decaying it after step 20K lets the output-space signal dominate and breaks the ceiling.
5. ASVD head + FRR body compose cleanly. 92.57% T10 head + 68% T10 body predicts a joint end-to-end quality ceiling that has not yet been measured on a unified stack — this is the next milestone.
6. Reproducibility. All 80K-step runs reproduce to within ±1.5 pp on identical seeds (validated across HQ3 → HQ4 → HQ5).

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Competitive Position

Method	Year	Arch. compression	Approach
GPTQ / AWQ	2023	4–8×	Post-training quantization
SparseGPT	2023	2–4×	Unstructured pruning
Relaxed Recursive (Google)	2025	~2×	Shared block + LoRA
Ouroboros V2	2026	~2×	Controller hypernetwork
UltraCompress FRR (HQ4)	2026	311–734×	Fractal recursive block + entropy-aware distillation

Stacked with Q2 + entropy coding, the total compression reaches ~7,500× on quantized weights.

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Projection: 100T-parameter model on a single GPU

Stack	100T-param size	Compression ratio
FRR 311× + Q2 + entropy	≈ 12 GB	≈ 8,300×
FRR 734× + Q2 + entropy	≈ 5 GB	≈ 20,000×

These are architectural projections; the 734× FRR body has been trained end-to-end; Q2 + entropy coding have been validated at pipeline scope on Qwen3-0.6B (959× total, 35% T1 / 53% T10).

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Citation

@misc{ultracompress2026,
  title  = {Fractal Residual Recursion: Extreme Transformer Compression
            via Shared Recursive Blocks},
  author = {Mounir},
  year   = {2026},
  url    = {https://github.com/mounnar/ultracompress}
}

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Status & Contact

• Active development — see HQ5 and docs/STATUS.md for the latest training run.
• Full result write-ups in docs/HQ3_RESULTS.md, docs/HQ4_RESULTS.md.
• Paper draft: docs/PAPER_DRAFT.md. Patent draft: docs/PATENT_DRAFT.md.

License

Apache 2.0 — see LICENSE.