Moons-Nano / README.md

betterwithage

Moons-Nano card

c2b7f19 verified about 22 hours ago

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metadata

license: apache-2.0
library_name: numpy
tags:
  - governed-ai
  - khipu
  - szl-holdings
  - moons
  - mlp
  - silhouette

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.

Bench (this tree)

TRAINING_RECEIPT.json seed 20260721 · steps 400 · honesty REPORTED

Metric	Value
acc	0.93
loss	~0.13
weights	`moons.npz` sha256 `dda50e3b293534de3f5aec01ebf9f8d6688e06069931618dfd35f01369904104`

Infers on POST /api/infer {"kind":"moons","x":0.2,"y":0.3}. Not 1.5B. Not a published benchmark.

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

Claim	Label	What-NOT
Weights trained in this package	REPORTED	silhouette, Not 1.5B
acc 0.93 on the training moons	REPORTED	not a published benchmark
Λ	ADVISORY · Conjecture 1 OPEN	never a theorem
Energy	UNAVAILABLE	never a fabricated joule
CUDA	UNAVAILABLE	CPU numpy LIVE