Operational (MEASURED laptop-Blackwell)

STATUS: tests PASS. get_kernel import-LIVE. Unsloth/LoRA is the wrong tool. Receipted kernels, not silent CUDA.

Thing	Label	Method / N / date / what-NOT
tests (PYTHONPATH=torch-ext)	PASS	MEASURED 2026-08-29T15:54:14Z host betterwithage Windows-10-10.0.26200-SP0. torch 2.10.0+cu128. GPU NVIDIA GeForce RTX 5050 Laptop GPU arch Blackwell. pytest 15 passed, 1 skipped in 2.66s. Failed nodes: none. What-NOT: not a leaderboard. torch.compile fullgraph failures on Windows Blackwell (cl is not found) are MEASURED, not hidden.
Kernel Hub get_kernel	import-LIVE	kernels 0.16.1. Default: get_kernel("SZLHOLDINGS/szl-receipt-attn", revision="main", trust_remote_code=True) → True. backend="cpu" → True. trust_remote_code=False → ValueError (SZLHOLDINGS is not a trusted publisher). repo_type=kernel required (kernels 0.16). What-NOT: not a weight load; do not pickle/joblib.load.
formula-tax	ADVISORY	locked-8 F1 F4 F7 F11 F12 F18 F19 F22. registry_count=21. Λ geomean 1.0. uniqueness Conjecture 1 (never a theorem).
I1–I8	catalog	I1 receipt-chain-continuity; I2 ledger-failure-shape; I3 served-run-has-model; I4 signed-columns-atomic; I5 loop-steps-positive; I6 receipt-ed25519-verify; I7 receipt-columns-consistent; I8 flywheel-lineage. Executed by SZLHOLDINGS/szl-invariants. Statuses never coerced. Λ untouched.
CUDA speedup / tokens/s / joules	UNAVAILABLE	Not claimed. Receipted kernels, not silent CUDA.

GitHub source: szl-holdings/szl-receipt-attn @ 1aa6cf77de5a789125c7961c6c9f1642ed7bd062. Artifacts: BENCH.laptop-blackwell.json, OPERATIONAL.json.

from kernels import get_kernel
k = get_kernel("SZLHOLDINGS/szl-receipt-attn", revision="main", trust_remote_code=True)

szl-receipt-attn

KANCHAY · Doctrine v11 · Lean 749/14/163 · Λ = Conjecture 1 (advisory) · a-11-oy.com

Kernel sources are on this repo. CPU get_kernel import-LIVE is MEASURED. GPU UNAVAILABLE (no cubin + timed run). Not a model. Not listed next to Chaski or Qantu.

Status

The cut

Guardrails after generation are late. Masking before softmax is the thing nobody ships because it hurts scores. We want the hurt.

A model that cannot attend to what it is not allowed to see.

Silhouette → leave → SZL

Leader	Take, then tweak
Anthropic	Constitution applied at the attention head.
NVIDIA	Fused kernel, NVIDIA-shaped, SZL-cut.
Unsloth	No.

Nobody else ships this combination. That is the point of a one-of-one.

Intended use

Fuse into governed decode.

Limitations

• Kernel, not weights.

Canonical GitHub: szl-holdings/szl-khipu

STATUS: import-LIVE on CPU Kernel Hub get_kernel (kernels 0.16.1). GPU UNAVAILABLE.

Thing	Label	Method / N / date / what-NOT
Kernel Hub get_kernel	import-LIVE	MEASURED 2026-08-28 2:29pm ET on kernels 0.16.1. HEAD 42d9a31 (42d9a31c5ca1749fca16017d73880cbc4c5050fc). Legal name szl-receipt-attn (Python module szl_receipt_attn). Variants: build/torch-universal (default get_kernel) and build/torch-cpu (backend="cpu"). Working calls: get_kernel("SZLHOLDINGS/szl-receipt-attn", revision="main", trust_remote_code=True) and the same with backend="cpu". selfcheck ok (max_abs_vs_sdpa=0.0, path=torch_reference, chain_ok=true). What-NOT: no tokens/s; no joules.
GPU / Triton	UNAVAILABLE	No cubin + timed GPU run this session. Not claimed LIVE. No tokens/s, no joules.

Canonical source: https://github.com/szl-holdings/szl-receipt-attn

This Hub repo is the publish mirror. ATELIER owns cards. No CUDA benches. Λ = Conjecture 1. Apache-2.0.

import torch
from szl_receipt_attn import receipt_attn, ReceiptChain, selfcheck

q = k = v = torch.randn(1, 2, 16, 32)
chain = ReceiptChain()
y = receipt_attn(q, k, v, causal=True, chain=chain)
print(chain.verify(), selfcheck())