wirkung/Origami-27B

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Origami-27B is a 27.80B-parameter Holographic Causal Graph Machine (HCGM) Preview compiled in closed form from Qwen/Qwen3.5-27B with zero retraining, zero fine-tuning, and zero gradient steps. It folds the 55.56 GB source checkpoint into 7.44 GB (7.46x compression), executes directly from NVMe/SSD storage via OS mmap with 0 GB GPU VRAM and 0 GB dedicated weight-RAM, and keeps live state strictly constant (O(1)) across arbitrary horizons on CPU, NPU, sGPU, Metal, and standalone PTX.

Origami-27B preserves the full Qwen/Qwen3.5-27B architecture (48 Gated DeltaNet layers, 16 full-attention layers (64 KV groups), rescued native Multi-Token Prediction (MTP), and a 248,320-entry vocabulary) while turning a passive weight checkpoint into a self-contained binary file executed under one cross-silicon contract. It succeeds our first-generation constant-state releases published under huggingface.co/tfwnotops by removing vLLM, MLX, and Transformers runtime dependencies altogether. This repository is a Preview release: structural byte footprints, fold ratios, and constant-memory invariants below are fixed properties of the compiled artifact, while hardware-dependent throughput figures are omitted from the card and documented in the Origami Family Paper.

pip install -U wirkung && wkng pull wirkung/Origami-27B -o ./Origami-27B
wkng run ./Origami-27B -p "Summarize how zero-retraining spectral folding preserves exact greedy trajectories." -n 128

Source checkpoint against Origami-27B

Property	Source (Qwen/Qwen3.5-27B)	Origami-27B (HCGM Preview)
Parameters	27.80B	27.80B (identical graph topology)
Retraining / fine-tuning steps	Pretrained baseline	0 (closed-form compilation)
Checkpoint footprint on disk	55.56 GB BF16	7.44 GB (7.46x smaller; 410 .o3f operator files (including rescued MTP) + exact_core.safetensors)
Required GPU VRAM	Multi-tens of GB HBM	0 GB (runs on plain CPU / NPU / sGPU or optional GPU)
Dedicated weight-RAM	Pinned in host / device RAM	0 GB (read-only OS mmap directly from SSD/NVMe)
Live session / horizon state	Linearly growing O(T) KV cache (68.72 GB at 1M tokens; 1.18 GiB in Gen-1 ConstantKV)	Constant 34.24 MB O(1) session state at any length (35x smaller than ConstantKV)
Operator & readout path	Dense per-step matrix scan	Certified Cauchy-Schwarz lm_head gate + rescued native MTP multi-row verification (0 greedy drift)
Runtime dependencies	PyTorch / vLLM / MLX / CUDA	wirkung + numpy (standalone C11, NPU, sGPU, Metal, PTX)

Figure 1. (a) Physical byte scale contrasting the 55.56 GB BF16 Qwen/Qwen3.5-27B checkpoint against the 7.44 GB Origami-27B .o3f bundle mapped directly from SSD (0 GB GPU VRAM, 0 GB dedicated weight-RAM). (b) Live conversation state across 4k to 1M tokens: linear O(T) KV growth against the flat 34.24 MB Origami-27B carrier.

Zero-retraining compilation and constant-state architecture

Every Origami model is produced by the same four-part compilation pipeline without gradient descent:

1. The file is the machine (disk-resident mmap execution). An HCGM model is an immutable binary artifact on disk. The operating system maps operator pages read-only on demand from SSD or NVMe storage, requiring neither GPU VRAM nor weight-deserialization buffers in host RAM. Under our v3-exakt dyadic integer reduction contract, CPU (AVX2 / NEON), general NPUs, embedded sGPU (cpu_cuda), Apple Metal, and driver-API PTX evaluate identical canonical reductions rather than drifting floating-point kernel approximations.
2. Closed-form spectral folding (.o3f, 7.46x without retraining) plus rescued native MTP. All 48 Gated DeltaNet blocks, 16 full-attention blocks, and the native Multi-Token Prediction (MTP) head are compiled directly from the frozen 55.56 GB BF16 checkpoint into 7.44 GB across 410 .o3f files (7.33 GB and 7.57x without MTP) via Rademacher-scrambled FWHT_128 bitplanes and rank-r projective crystals (U_rV_r^⊤), fitting comfortably on 16 GB consumer nodes without swap.
3. Constant 34.24 MB O(1) state synthesized via Wirkung. Using Wirkung, our internal exact structural-law and closed-form operator synthesis engine, all 48 Gated DeltaNet layers execute via associative WY monoid scans while all 64 full-attention KV groups across the 16 global layers are bounded by a pinned Token-0 sink, an exact 1,024-token frontier, and key-centered Ward centroids, cutting live state from 1.18 GiB in our first-generation ConstantKV build to 34.24 MB at every sequence horizon.
4. Certified readout and online trajectory decomposition (X = X_known + R). Rather than re-evaluating every parameter blindly for isolated single tokens, HCGM decomposes live hidden trajectories into known low-rank structure X_known (cosine trajectory banks, multi-order suffix tables, and rank-1 Sherman-Morrison-Woodbury updates) plus novel residual R, verifying multiple candidate steps in one weight pass with zero greedy output drift.

Figure 2. (a) Constant-memory attention read over token coordinate j: pinned sink s₀, bounded Ward + WY Gated DeltaNet carrier, and exact local frontier (W = 1024). (b) Single-pass .o3f contraction pairing 4-class Walsh-Hadamard bitplanes with a rank-r projective crystal (U_rV_r^⊤ + rescued MTP) and Cauchy-Schwarz pruning.

Shared trajectory assimilation (M-Rail), GPU-less server fleets and multi-model routing (HMoE)

Beyond single-node execution, Origami-27B attaches natively to Shared M-Rail (.mrail v3), a content-addressed associative trajectory manifold shared across processes or across an enterprise network:

• Warm up once on one GPU, run permanently on disk-only CPU/NPU servers. Because HCGM models require only SSD storage and megabytes of live state, an organization can run a single GPU (or a short temporary cloud GPU lease) until its recurring domain workflows are crystallized into a shared .mrail file, then shut the GPU down and serve frontier models across commodity CPU/NPU VMs and CPU-only servers. Known trajectories execute via Track copy morphisms or resume in O(1) space from Station (ANCHOR + DELTA) snapshots; whenever any server encounters an unseen task (R ≠ 0), computing and committing that residual once makes the solution immediately available to every other node on the rail.
• Heavy Mixture of Experts (HMoE). Because idle disk-mapped models consume zero weight-RAM, M-Rail routes across complete compiled Origami models (Origami-9B, Origami-27B, Origami-31B, Origami-78B, Origami-H3), zero-recompile static-graph NPU specialist pools, and sub-2 KB exact algebraic group organs on a synchronous station bus (cstar) under deterministic check gates.

Figure 3. (a) Shared M-Rail (.mrail v3) fleet architecture: optional temporary GPU warmup feeds a permanent zero-GPU CPU/NPU/sGPU server fleet where any novel residual +R solved once commits back to all nodes alongside HMoE routing on cstar. (b) Total resident RAM/VRAM across N = 1..32 concurrent 32k-token sessions: pinned weights plus N · KV against shared read-only mmap plus N · O(1) constant state.

How to run

Requires Python 3.10+ and the wirkung runtime package (pip install -U wirkung), which installs the wkng, wirkung, sgpu, and rail CLI entrypoints.

pip install -U wirkung
wkng pull wirkung/Origami-27B -o ./Origami-27B
wkng verify ./Origami-27B --full
wkng run ./Origami-27B --chat
wkng serve ./Origami-27B --host 127.0.0.1 --port 8080

from wirkung import Engine

eng = Engine("./Origami-27B")
print(eng.generate("Outline a zero-GPU enterprise server deployment over Shared M-Rail:", max_new_tokens=128))

Files

• layers/*.o3f, mtp/*.o3f, vocab/*.o3f: 410 folded HCGM operator files including rescued native MTP (7.44 GB total bundle)
• exact_core.safetensors, mtp/mtp_exact.safetensors: exact norms and structural scalars
• bundle.json, origami27b_manifest.json: shard inventory and SHA-256 digests verified by wkng verify
• config.json, generation_config.json, tokenizer.json, tokenizer_config.json, chat_template.jinja: tokenizer and model configuration
• paper/Wirkung_2026_Origami_HCGM.pdf: the Origami Model Family technical report
• assets/: banner.png, architecture.png, mechanism.png, mrail.png
• LICENSE, NOTICE: Wirkung Model and Runtime License 1.0 and upstream attribution

Inquiries and collaboration

We welcome inquiries regarding custom zero-retraining HCGM model compilation, closed-form formula and exact algorithm synthesis via Wirkung, Shared M-Rail / zero-GPU enterprise fleet deployments, and research collaboration:

• Email: contact@wirku.ng
• X (Twitter): https://x.com/FossDT
• Web: https://wirku.ng

Papers

• The Origami Model Family: Compiling Pretrained Foundation Models into Holographic Causal Graph Machines. David Tom Foss, 2026. DOI 10.13140/RG.2.2.31826.72647. Included directly in this repository at paper/Wirkung_2026_Origami_HCGM.pdf.
• Holographic Causal Graph Machines: Source-Free Constant-State Execution of Pretrained Transformers. David Tom Foss, 2026.
• Crystallized Attention: Constant-Memory Exact Store and Exponential-Mode Carriers. David Tom Foss, 2026.
• Intelligence Is Not Made of Scarcity: Constant-Memory Causal Execution and Associative M-Rail. David Tom Foss, 2026.

Citation

@techreport{foss2026origami,
  title       = {The Origami Model Family: Compiling Pretrained Foundation Models into Holographic Causal Graph Machines},
  author      = {Foss, David Tom},
  institution = {Wirkung Inc.},
  year        = {2026},
  month       = nov,
  doi         = {10.13140/RG.2.2.31826.72647},
  url         = {https://doi.org/10.13140/RG.2.2.31826.72647}
}

License

Released under the Wirkung Model and Runtime License 1.0. Compiled in closed form without training steps from Apache-2.0 (Qwen/Qwen3.5-27B); full attribution and closed-form transformation notices are recorded in NOTICE.