README.md

metadata

license: apache-2.0
library_name: numpy
tags:
  - governed-ai
  - khipu
  - szl-holdings
  - embedding
  - silhouette
  - software
  - reference
  - test-fixture

MiniEmbed-Nano

A deterministic 64 × 12 NumPy table for inspecting hash-based token vectors and simple pooling.

Artifact: NumPy embedding table · Stage: Software / reference / test fixture

Explore in Command Lab · Build · Evidence

Before you use it

• This is a small reference table, not a trained neural embedding model or the separate 3290 × 128 SVD MiniEmbed artifact.
• The example constructs a new table; it does not verify loading or reproducing the published archive.
• No retrieval or analogy score is established for this artifact. Bind its exact revision, array schema, and loader before comparison.

Technical details and evidence

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.

MiniEmbed-Nano

Tiny hash+table embed: V=64, d=12, L2-normalized rows. Not a foundation embed. Not neural. Not MiniEmbed 3290×128.

Canonical source: szl-holdings/szl-khipu
Sibling card: SZLHOLDINGS/szl-khipu
The larger statistical MiniEmbed (3290 × 128) lives on SZLHOLDINGS/szl-kernels — a different artifact. Do not mix them.

Construction example for the canonical package: this builds and saves a new deterministic 64 × 12 table. It does not load or independently verify the published mini_embed.npz.

from szl_khipu.train import mini_embed

emb = mini_embed.build(seed=20260721)
vec = emb.embed("knot the run")
print(emb.V, emb.D, vec.shape)
# 64 12 (12,)
emb.save_npz("mini_embed.npz")

What it does

• SHA-256 token id modulo 64. Mean-pool then L2. Deterministic given seed.
• Built here on CPU NumPy. Honesty REPORTED. Energy UNAVAILABLE.
• No analogy score. No retrieval score. No SVD variance claim (that belongs to the 3290×128 table).

Reported synthetic fixture evidence

TRAINING_RECEIPT.json seed 20260721 · honesty REPORTED

Metric	Value
V×d	64 × 12
method	hash+table L2
weights	mini_embed.npz receipt-reported sha256 ae31a3a7214d1f142d8ea3f4f86c35bdedd7c108bc5d04ea00c87e7b674e6e3b

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 the SZLHOLDINGS/szl-kernels MiniEmbed (3290 × 128, SVD var 0.3146).
• Not neural. Not word2vec. Not a foundation embed.
• Not 1.5B. Not Qwen.
• Not proven trust. Λ uniqueness remains Conjecture 1 OPEN.
• Energy UNAVAILABLE. CUDA UNAVAILABLE. Never a fabricated joule.

Honesty

Claim	Label	What-NOT
Table built in this package	REPORTED	V=64 d=12, not 3290×128
Neural / trained embed	FALSE	hash+table, not SGD
Analogy / retrieval score	UNAVAILABLE	not measured
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.

Artifact evidence

The previous card reports mini_embed.npz (6,892 bytes). Receipt-reported SHA-256 (not rehashed in this review):

ae31a3a7214d1f142d8ea3f4f86c35bdedd7c108bc5d04ea00c87e7b674e6e3b

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.

Array	Shape	Data type
table	[64, 12]	float64
V	[]	int64
D	[]	int64

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 01e82f36ff722528233f76daf899c18f8cb5aaa2. Reviewed publisher source: hf/MiniEmbed-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["mini_embed.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.