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 2026-08-29T15:54:08Z host betterwithage Windows-10-10.0.26200-SP0. torch 2.10.0+cu128. GPU NVIDIA GeForce RTX 5050 Laptop GPU arch Blackwell. pytest 25 passed, 1 skipped in 0.33s. 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/YARQA-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/YARQA-ATTN @ 160640bd8ed138e0170838c5a0de470ba8539367. Artifacts: BENCH.laptop-blackwell.json, OPERATIONAL.json.

from kernels import get_kernel
k = get_kernel("SZLHOLDINGS/YARQA-ATTN", revision="main", trust_remote_code=True)

YARQA-ATTN

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

Python kernel is on this repo. CPU get_kernel import-LIVE MEASURED (7e533ce). GPU cubins UNAVAILABLE (not ROADMAP). Not an alias of szl-receipt-attn. Not a fourth Flash / Flex / paged stack.

Status

The cut

FlashAttention is faster. YARQA is accountable. We steal the kernel discipline from NVIDIA and spend it on provenance, not FLOPs.

An attention op whose softmax support is reconstructable from a signed log.

Silhouette → leave → SZL

Leader	Take, then tweak
Anthropic	Interpretability as a runtime artifact.
NVIDIA	cuDNN / FlashAttention silhouette — then we add the receipt.
Unsloth	Unrelated. Don't wrap this in FastLanguageModel.

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

Intended use

Drop-in attention with an audit tape.

Limitations

• Not a checkpoint.
• Performance vs FlashAttention is not claimed.

Canonical GitHub: szl-holdings/szl-khipu

STATUS: import-LIVE on CPU Kernel Hub get_kernel (kernels 0.16.1). GPU cubins UNAVAILABLE this session (not ROADMAP).

Thing	Label	Method / N / date / what-NOT
Kernel Hub get_kernel	import-LIVE	MEASURED 2026-08-28 3:08pm ET on kernels 0.16.1. Package HEAD 7e533ce (7e533ce702029061bc68f9f9cafe88efdd7f5f00). README at MEASURE 2871b3c. Legal name yarqa-attn (Python module yarqa_attn). Variants: build/torch-universal (default get_kernel) and build/torch-cpu (backend="cpu"). Working calls: get_kernel("SZLHOLDINGS/YARQA-ATTN", revision="main", trust_remote_code=True) and the same with backend="cpu". selfcheck ok. max_abs_vs_compartment_ref=3.58e-07 (full 3.5762786865234375e-07), path=torch_compartment. What-NOT: no tokens/s; no joules; not a fourth Flash / Flex / paged stack. Lambda = Conjecture 1 (advisory).
GPU cubins	UNAVAILABLE	MEASURED 2026-08-28 7:01pm ET this session. Host cursor (Linux 6.12.94+ x86_64, Intel Xeon 8-core). torch 2.13.0+cu130 compiled CUDA 13.0. torch.cuda.is_available()=false. nvidia-smi UNAVAILABLE. device_count=0. Triton 3.7.1 present with no CUDA device. No cubin shipped. No tokens/s. No joules. CPU import-LIVE unchanged. Not a fourth Flash / Flex / paged stack. Lab stays Khipu.

KERNEL kernel card. Original SZL compartment / plug-flow attention cut. Receipt-aware. Honesty-labeled.

Not a Fall 2026 ATELIER weight. No tensors in this repo. Not an alias of szl-receipt-attn. Not a pointer at the Triton trio (szl-receipt-attn, szl-maskmod, szl-block-kv). Those three stay separate. a11oy-net does not list this as a fourth Flash / Flex / paged stack.

GitHub is source of truth: szl-holdings/YARQA-ATTN. KERNEL binds Hub bytes from that tree. Do not PUT an empty card.

Owner	KERNEL
Artifact	kernel (Python present; no weights; GPU cubins not claimed)
Status	import-LIVE CPU · GPU cubins UNAVAILABLE
License	Apache-2.0
Λ	Conjecture 1 (advisory, never a theorem)
Path	torch_compartment (CPU)
Serve studio	not this repo. Live CPU lab is szl-model-inference-lab (Khipu GGUF only)

Silhouette: partition a sequence into canals (contiguous compartments), attend within a canal, emit SHA3-256 of the partition and of the attention output. We do not copy Dao hopper, Sage csrc, vLLM paged .cu, cuDNN FMHA, TRT cubins, CuTeDSL, or flex_attention.py. Metaphor only vs szl-holdings/yarqa (CFD; different product). Throughput is MEASURED only from a timed run on named hardware. Until then every speed claim is unstamped. No tokens/s. No joules.

Do not list this next to Chaski, Qantu, Waman, Chakana, or Tinku.

Load

from kernels import get_kernel
attn = get_kernel("SZLHOLDINGS/YARQA-ATTN", revision="main", trust_remote_code=True)

Fashion GO 2026-08-28 3:10pm ET. import-LIVE CPU stays. GPU cubins stamped UNAVAILABLE 2026-08-28 7:01pm ET (no CUDA device this session). Not a fourth Flash / Flex / paged stack.

Apache-2.0. Copyright 2026 SZL Holdings.