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.

TinyKhipu-Nano

Token embeddings and handles in. NAVIGATE or ABSTAIN out. Abstain is the default class, not a post-hoc filter.

Family. nano · Evidence. SYNTHETIC · Weights. numpy · Architecture. 24-token, 12-dimensional embedding with two-class and handle-scoring heads · Not 1.5B.

Hub: SZLHOLDINGS/TinyKhipu-Nano

The cut

Leaders train models to answer. We train a silhouette to shut up when overlap is thin or the lure is adversarial. This nano is the token-and-handle reference model of that cut. Not 1.5B.

A synthetic navigator that mean-pools token embeddings, scores handle notes, and returns NAVIGATE or ABSTAIN.

Silhouette → leave → SZL

Leader	Take, then tweak
Anthropic	Refusal as a typed output, not a polite paragraph.
NVIDIA	Guardrail inside the head, not a sidecar.
Unsloth	The 1.5B QLoRA is the grown form of this MLP.

Nobody else ships this combination. That is the point of a one-of-one.

Intended use

Unit-test the NAVIGATE|ABSTAIN schema before GPU spend.

Bench (this tree)

TRAINING_RECEIPT.json seed 20260721 · steps 280 · honesty REPORTED

Metric	Value
plan_valid	1.00
abstain	1.00
hallucinated	0
weights	tiny_khipu.npz sha256 cc8d0385b2c75079669df809d7e4823f1ad8d9d535aec511446347490b11dff9

Infers on POST /api/infer {"kind":"tiny_khipu"}. Hard ID filter. Not Qwen. Not 1.5B.

Limitations

• Synthetic features. Perfect holdout is a design fact, not a field claim.
• The 1.5B abstain rate is 2/6 — this nano does not wash that.

Honesty

Claim	Label
This card's numbers	SYNTHETIC
Energy / joules	UNAVAILABLE unless a signed meter says MEASURED
Λ uniqueness	Conjecture 1 OPEN — not a theorem
GGUF as the signed object	FALSE

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

Apache-2.0. Copyright 2026 SZL Holdings · Stephen P. Lutar Jr. · ORCID 0009-0001-0110-4173.

Artifact evidence

tiny_khipu.npz is present (3,568 bytes). SHA-256:

cc8d0385b2c75079669df809d7e4823f1ad8d9d535aec511446347490b11dff9

The archive hash matches TRAINING_RECEIPT.json. Its arrays were inspected with numpy.load(..., allow_pickle=False); numeric values were finite.

Array	Shape	Data type
E	[24, 12]	float64
W	[2, 12]	float64
b	[2]	float64
Wc	[12]	float64

A matching unsigned receipt establishes local artifact consistency. Training metrics remain reported synthetic results; this check does not independently reproduce training or establish deployment, general intelligence, or production readiness.