szl-khipu

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SZL Khipu

Inspect advisory NumPy kernels, small reference fixtures, and hash-chained run receipts.

Artifact: Python / NumPy software package · Stage: Software / reference

Explore in Command Lab · Build · Evidence

Before you use it

• Λ is advisory: Conjecture 1 OPEN and proven_trust=false.
• CUDA and energy measurement are UNAVAILABLE; software source binding does not establish a production deployment.
• The package and Nano fixtures do not qualify a 1.5B checkpoint. Verify each artifact and its separate inference contract.

<details> <summary>Technical details and evidence</summary> <!-- SZL-CARD-TECHNICAL:v1:START -->

szl-khipu

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Control before action. Evidence after.

Part of the szl-holdings estate · Product: a-11-oy.com · Proof: a11oy.net

<!-- /szl:header -->

Knot the run. Hash the proof. Fail closed.

Governed-AI khipu: an advisory Λ-gate, original YARQA canal attention, a TinyKhipu NAVIGATE/ABSTAIN silhouette, and hash-chained receipts. CPU numpy is LIVE. CUDA is UNAVAILABLE. energy UNAVAILABLE. proven_trust is always false.

Canonical GitHub source: szl-holdings/szl-khipu
Hub card: SZLHOLDINGS/szl-khipu
Kernels: SZLHOLDINGS/szl-khipu-kernels

The canonical publish-hf workflow mirrors szl_khipu/, README.md, LICENSE, and pyproject.toml from immutable Git blobs in one Hub model commit. Its MODEL_SOURCE_BINDING.json records the Git commit, tree, and each file digest; publication succeeds only after immutable provider readback matches every byte. Historical Hub files outside this software scope are preserved and are not attested by this binding. Software parity makes no trained-checkpoint claim.

What it is

• A Python package (szl_khipu) that knots a run, hashes a receipt, and fail-closes on a zero axis or a broken chain.
• Λ: weighted geometric mean over 13 Yuyay axes, advisory, never proven trust.
• YARQA: contiguous canals; attend only inside the compartment; leak is the bound.
• TinyKhipu-Nano: NAVIGATE / ABSTAIN silhouette. A few thousand floats. Hard ID filter.
• ReceiptAgent-Nano: the reviewed szl_khipu/train/receipt_agent.py defines 0=ALLOW, 1=WARN, 2=BLOCKED, 3=ESCALATE. The deterministic rule_check decision remains authoritative; learned predictions do not override it. This software binding does not attest the sibling archive, its card, or its historical training/class-index contract.
• Receipts: sha256 of weights, seed, steps, loss. Honesty REPORTED. Joules null.
• Organ integrity: five-organ fail-closed kernel of szl-holdings/anatomy. HEART/YUYAY, YAWAR, YACHAY, OTel, Khipu skeleton. Not a Three.js rehost. The 3D atlas is SLSA L1 static viz.

What it is NOT

• Not Qwen. Not a Qwen fine-tune, not a Qwen rehost, not a Qwen eval.
• Not 1.5B. Nothing in this tree trains or publishes SZL-Khipu-1.5B, KHIPU-R2, or ReceiptAgent 1.5B. Those Hub cards stay RESEARCH and are not produced here.
• Not a FlashAttention rehost. Not Dao .cu, not hopper/, not cute/, not Sage csrc, not vLLM paged .cu, not flex_attention.py.
• Λ uniqueness is OPEN. Conjecture 1 is not a theorem. Unconditional uniqueness under kernel A1–A5 is machine-checked FALSE.
• Energy UNAVAILABLE. Never a fabricated joule. MEASURED-NVML or honest null.
• Not trained weights at 1–2B. Not GGUF. Not a CUDA speedup. Not proven trust.

Install

pip install -e .
pip install -e ".[gradio]"   # optional — Gradio 6 demo; gradio>=6.27.0,<7
pip install -e ".[torch]"    # optional — torch path; CUDA still UNAVAILABLE here

Requires Python ≥ 3.11. Runtime dep: numpy>=1.26. Apache-2.0.

Python frontend + backend

Stdlib HTTP. Same process serves the holographic UI and runs the kernels. No Gradio required.

szl-khipu serve --host 0.0.0.0 --port 7860
# GET  /healthz  /version  /api/lambda  /api/greenlight  /api/anatomy
# POST /api/lambda  /api/anatomy  /api/greenlight  /api/yarqa  /api/tiledigest

Gold = OPEN. Proof teal = LIVE. Never green-as-proven. Energy UNAVAILABLE. Conjecture 1 stays OPEN.

Static JS silhouette (not the NumPy kernel): docs/index.html · szl-holdings/khipu-pages · htmlpreview.

Quickstart

Lambda (advisory)

from szl_khipu import evaluate_lambda, YUYAY_FLOORS

ev = evaluate_lambda(list(YUYAY_FLOORS))
print(ev.value, ev.blocked, ev.reason)
# advisory pass — uniqueness remains Conjecture 1 OPEN

zeroed = list(YUYAY_FLOORS)
zeroed[0] = 0.0
print(evaluate_lambda(zeroed).blocked)  # True — fail closed

CLI:

szl-khipu demo-lambda
szl-khipu demo-lambda --zero 0

YARQA (canal leak)

import numpy as np
from szl_khipu import yarqa_attn

q = k = v = np.random.default_rng(7).standard_normal((12, 4))
out = yarqa_attn(q, k, v, n_canals=3)
print(out.leaked)  # bound: leaked ≤ 1e-9

szl-khipu demo-yarqa --n-canals 3

For numerical output without dense probability diagnostics, use yarqa_attn_output(q, k, v, n_canals=3). This NumPy path computes query tiles inside each canal and avoids full sequence-square score/probability matrices. The API contract and reproducible CPU comparison document validation, memory limits, attribution, and measurement boundaries at the reviewed source commit. This document is outside the model software mirror scope and is not included in that Hub model snapshot.

Organ integrity (five organs)

Fail-closed substrate. Any DOWN organ or a WILLAY veto blocks the body. Λ stays Conjecture 1 OPEN. Energy UNAVAILABLE. Locked-proven stays 8. Not a Three.js rehost.

from szl_khipu import evaluate_anatomy

ev = evaluate_anatomy(seed=11)
print(ev.live_count, ev.blocked, ev.energy)  # 5 False UNAVAILABLE

print(evaluate_anatomy(zero_heart=True).blocked)        # True
print(evaluate_anatomy(fabricate_joule=True).energy_j)  # None — never a fabricated joule

szl-khipu demo-anatomy
szl-khipu demo-anatomy --tamper-chain
szl-khipu demo-anatomy --fabricate-joule

Canonical anatomy repo: szl-holdings/anatomy · 3D atlas: SZLHOLDINGS/anatomy

Train TinyKhipu

szl-khipu train --seed 20260721 --steps 280 --out artifacts
# writes artifacts/tiny_khipu.npz + artifacts/training_receipt.json
szl-khipu verify artifacts/training_receipt.json

from szl_khipu import train_tiny_khipu

result = train_tiny_khipu(seed=20260721, steps=280)
print(result.plan_valid, result.abstain, result.hallucinated)

Export all three silhouettes (TinyKhipu, ReceiptAgent, moons) and a combined receipt:

python scripts/train_and_export.py
# artifacts/*.npz + artifacts/TRAINING_RECEIPT.json
# honesty REPORTED · energy_j null · proven_trust false · whatNot listed

Kernels vs field leaders (honest deltas)

This repo silhouettes Λ, YARQA, TinyKhipu, ReceiptAgent, and receipts in numpy. It does not replace those field leaders and does not inherit their benches.

Locked-8

Lean locked-proven formulas = exactly 8. Lab numerics CHECKED ≠ Lean PROVEN. The F-number mapping onto any larger registry is UNKNOWN — never fabricated.

Conjecture 1

Any two aggregators satisfying A1–A4 agree on every input. OPEN (sorry). Unconditional uniqueness under kernel A1–A5 is machine-checked FALSE.

Λ is advisory. Trust ceiling 0.97. proven_trust is false in this artifact and every receipt it emits.

Axioms carried at runtime (checks, not a uniqueness proof):

Honesty

Doctrine v11 LOCKED · 749 declarations · 14 axioms · 163 tracked sorries · locked-proven 8.

Hub siblings

License

Apache-2.0. Copyright 2026 SZL Holdings.

Stephen P. Lutar Jr. / SZL Holdings · ORCID 0009-0001-0110-4173

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Work with SZL Holdings

szl-khipu participates in the SZL governed-AI estate. The flagship control plane targets governed agent change management, but production/runtime qualification is tracked separately through the GitHub → Hugging Face → product → proof evidence chain. This repository does not itself prove or authorize a production deployment.

• Design partners (6-month, paid): governed-action receipts in your environment → [email protected]
• Verify our claims offline: github.com/szl-holdings/szl-gov — signed estate receipt + public verifier
• Pricing + SKUs: not maintained in this repository.

<!-- SZL-CARD-TECHNICAL:v1:END --> </details>