Moons-Nano

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Moons-Nano

A 2→8→2 NumPy classifier for studying a small synthetic two-moons decision boundary.

Artifact: Bare NumPy weight archive · Stage: Software / reference / test fixture

Explore in Command Lab · Build · Evidence

Before you use it

• Training accuracy and loss are historical reported fixture results; they do not establish held-out performance or generalization.
• The example constructs a new fixture rather than loading the published archive. Match its array schema and loader before use.
• This toy fixture does not qualify a larger model. CUDA and energy measurement are UNAVAILABLE.

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

Status: SOFTWARE / REFERENCE / TEST FIXTURE. Not a production model.

This Hub repository contains a bare NumPy archive. The loading and forward-pass implementation lives in the canonical szl_khipu package; no packaged Hub loader or config.json is shipped alongside these weights. Treat this as a software fixture until its complete inference contract is independently verified.

Moons-Nano

Two-moons 2→8→2 tanh-softmax SGD. A few hundred floats. Not 1.5B. Not Qwen. Not a foundation model.

Canonical source: szl-holdings/szl-khipu
Sibling card: SZLHOLDINGS/szl-khipu

from szl_khipu.train import moons

weights, ev = moons.train(seed=20260721, steps=400)
print(ev["acc"], ev["loss"])
# REPORTED: acc 0.93 · loss ~0.13 on the training moons
moons.save_npz("moons.npz", weights)

What it does

• Classic two-moons toy classification. Hidden width 8. Softmax over 2.
• Trained here on CPU NumPy. Honesty REPORTED. Energy UNAVAILABLE.

Reported synthetic fixture evidence

TRAINING_RECEIPT.json seed 20260721 · steps 400 · honesty REPORTED

Accuracy 0.93 and loss 0.12973121797997034 are reported on the training moons. They are not held-out generalization or a published benchmark. The construction example above trains a new fixture; it does not load this archive.

The related demo documents an application-specific POST /api/infer route. This archive repository establishes no hosted endpoint, served revision, or deployment guarantee. The route is illustrative application context.

What it is NOT

• Not SZL-Khipu-1.5B. Not QLoRA. Not a chat model.
• Not sklearn moons as a product claim. A live silhouette so the estate has a TRAINED tiny MLP that actually ran.
• Not proven trust. Λ uniqueness remains Conjecture 1 OPEN.
• Energy UNAVAILABLE. CUDA UNAVAILABLE. Never a fabricated joule.

Honesty

Doctrine v11 LOCKED · 749/14/163 · locked-proven 8. Apache-2.0. Copyright 2026 SZL Holdings · Stephen P. Lutar Jr. · ORCID 0009-0001-0110-4173.

Artifact evidence

The previous card reports moons.npz (1,302 bytes). Receipt-reported SHA-256 (not rehashed in this review):

dda50e3b293534de3f5aec01ebf9f8d6688e06069931618dfd35f01369904104

The previous card reported that the archive matched the unsigned training receipt and that numpy.load(..., allow_pickle=False) found finite numeric arrays. The table below preserves that historical report. The September 30, 2026 card review read pinned text and the receipt; it did not download, rehash, or inspect the archive, and did not replay training.

The retained receipt labels these reported synthetic fixture results REPORTED.

An independently checked archive/receipt match could establish local artifact consistency; an unsigned digest would still not authenticate authorship or measurement. These preserved receipt and array reports establish no new training replay, independent evaluation, deployment, or production readiness.

Reviewed Hub text: immutable snapshot 77d002f9314dc0c86cd0f14e63fff60364929652. Reviewed publisher source: hf/Moons-Nano/README.md at e53e3d24b22e356eb986c373aee27b3b3e7947ec. The shared unsigned TRAINING_RECEIPT.json, timestamped 2026-08-29T17:11:32.518042+00:00, enumerates four artifacts. Only its artifacts["moons.npz"] entry describes this archive; the other entries do not establish that sibling artifacts are present in this repository. The receipt does not bind its training run to the reviewed source commit.

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