| --- |
| license: apache-2.0 |
| library_name: numpy |
| tags: |
| - governed-ai |
| - khipu |
| - szl-holdings |
| - moons |
| - mlp |
| - silhouette |
| - needs-loader |
| - test-fixture |
| --- |
| |
| > ### How to actually load this — the weights alone are not enough |
| > |
| > `moons.npz` is a real, honest 2-8-2 MLP produced by a real training run, and the card |
| > below does not overstate it. But it is a bare NumPy archive with **no |
| > `config.json` and no loader in this repo**, so `from_pretrained` and the Hub |
| > inference widget cannot touch it. Nothing here tells you the array names or the |
| > forward pass. |
| > |
| > ```python |
| > import numpy as np |
| > from huggingface_hub import hf_hub_download |
| > |
| > path = hf_hub_download("SZLHOLDINGS/Moons-Nano", "moons.npz") |
| > w = np.load(path) |
| > print(sorted(w.files)) # array names are the de-facto interface |
| > ``` |
| > |
| > The forward pass this was trained against lives in the `szl_khipu` package, in |
| > [SZLHOLDINGS/szl-khipu-kernels](https://huggingface.co/SZLHOLDINGS/szl-khipu-kernels) |
| > — a **different repository**. Until the loader ships alongside the weights (or a |
| > `custom_code` handler is added), treat this repo as a **test fixture**, not a |
| > deployable model. Evidence status: acc 0.93 / loss 0.13 REPORTED on the TRAIN set. |
|
|
| # 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](https://github.com/szl-holdings/szl-khipu) |
| Sibling card: [SZLHOLDINGS/szl-khipu](https://huggingface.co/SZLHOLDINGS/szl-khipu) |
|
|
| ```python |
| 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 | |
|
|
| 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](https://orcid.org/0009-0001-0110-4173). |
|
|
