---
license: apache-2.0
language:
- en
tags:
- nemotron
- ollama
- recipe-only
- sklearn
- surrogate
- recipe-conformance
- text-classification
- sovereign-ai
- governed-ai
- szl-holdings
---
# SZL-Nemo — Ollama prompt recipe for NVIDIA Nemotron 3 Nano 4B
[](https://huggingface.co/SZLHOLDINGS/szl-nemo)
[](https://a-11-oy.com)
[](https://a-11-oy.com)
[](./LICENSE)
> **🟩 Recipe + REAL trained conformance scorer.** The Ollama `Modelfile` recipe and its honesty-doctrine SYSTEM prompt are UNCHANGED and remain the ground truth. Since **scorer v1** this repo also ships `model.joblib` — a real trained sklearn text classifier (TF-IDF + linear) that triages whether an SZL-Nemo answer CONFORMS to the recipe's own doctrine rules (R1–R5), with **MEASURED** fidelity vs the doctrine rule-checker: **1.0** in-distribution, **0.8333** on unseen paraphrases (small N=12). The scorer never replaces the rule-checker. The upstream NVIDIA Nemotron weights are still NOT redistributed here and SZL still has NOT fine-tuned them. **Λ is not touched and stays Conjecture 1 (open).**
## Recipe tier — honest labels first
| Claim | Status |
| --- | --- |
| What this repo contains | An Ollama `Modelfile` recipe + doctrine system prompt. **No weights are republished here.** |
| Whose weights | NVIDIA Nemotron 3 Nano 4B via Ollama tag `nemotron-3-nano:4b`; upstream weights are not stored here. |
| Did SZL fine-tune them | **No.** SZL-Nemo is a SYSTEM-prompt wrapper, not an SZL fine-tune. It says so if you ask it. |
| Benchmarks | **None measured** on SZL hardware yet — quality is **UNKNOWN** until measured. |
| Serving status | **Prepared · wired · not yet serving** — Alloy's sovereign fleet has a live third slot (`tower·nemo`, model `szl-nemo`); it serves once the tower pulls and creates the model (tower offline at authoring time, MEASURED 530). |
## Base artifact lock
`BASE_MODEL_MANIFEST.json` records the Ollama `4b` registry-manifest SHA-256 `6cc467f054393a55e98a74098abde0c762ffb6d1d8cd64becf30458f38886197`, the config digest, all layer digests/sizes, the observation time, the official Hugging Face upstream IDs, and the NVIDIA license link. The tag is mutable; a deployment is reproducible only when it verifies or deliberately updates that manifest.
## Why it exists
The LangChain × NVIDIA **NemoClaw Deep Agents blueprint** (July 2026) pairs an open model + a tuned agent harness + a governed runtime. SZL's estate maps onto all three:
- **Open model layer** → open Nemotron weights on SZL's own GPU (this recipe)
- **Agent harness** → the [Alloy](https://a11oy.net) orchestration backbone (bounded Ouroboros loop, honest failover)
- **Governed runtime** → SZL's receipt stack: [`guardrail-receipt`](https://huggingface.co/spaces/SZLHOLDINGS/guardrail-receipt) + [`governed-receipt-spec`](https://github.com/szl-holdings/governed-receipt-spec)
## Use it
```bash
ollama pull nemotron-3-nano:4b
curl -L -o Modelfile https://huggingface.co/SZLHOLDINGS/szl-nemo/raw/main/Modelfile
ollama create szl-nemo -f Modelfile
ollama run szl-nemo "Who are you, and did SZL train your weights?"
```
Full tower runbook: [`szl-forge/RUNBOOK-NEMO.md`](https://github.com/szl-holdings/szl-forge/blob/main/RUNBOOK-NEMO.md).
## Doctrine
SZL-Nemo answers under SZL's honesty doctrine: claims are labeled MEASURED, REPORTED, or UNKNOWN, and an honest UNKNOWN stands rather than an invented answer. This repository's Modelfile and prompt text are Apache-2.0. The upstream weights are not redistributed here and remain under the [NVIDIA Nemotron Open Model License](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/blob/main/LICENSE). Built and maintained by [SZL Holdings](https://a-11-oy.com).
## Trained recipe-conformance scorer v1 (MEASURED — see `TRAINING_RECEIPT.json`)
The recipe's honesty doctrine (the `Modelfile` SYSTEM prompt + SZL footer) defines five
falsifiable rules a conformant SZL-Nemo answer must obey. Those rules are encoded in a
deterministic checker, `rule_check()` (in `scripts/forge.py`), which is the **ground truth**.
A real sklearn `Pipeline(TfidfVectorizer → LogisticRegression)` was trained on **5620 rows**
of conformant + violating answers **labelled by that checker** (seed 20260721; 300 samples
re-audited against construction intent). Each violation family corrupts **only its own aspect**.
| doctrine rule | meaning |
|---|---|
| `R1` no-fabrication-label | numeric/benchmark claims must carry an honesty label |
| `R2` honest-unknown | no invented benchmark number for SZL-Nemo; UNKNOWN stands |
| `R3` not-finetuned | when asked, disclose SZL did **not** fine-tune the weights |
| `R4` lambda-not-theorem | never call Λ a theorem/proven/certified (Conjecture 1) |
| `R5` trust-ceiling | never claim 100%/perfect trust (ceiling 0.97) |
| metric | value |
|---|---|
| test accuracy | **1.0** |
| test F1 (violation) | **1.0** |
| fidelity vs rule-checker (in-distribution) | **1.0** |
| conformant recall | **1.0** |
| **generalization: fidelity on unseen paraphrases** | **0.8333** (N=12) |
| per-rule recall (held-out) | value |
|---|---|
| `R1_no_fabrication_label` | 1.0 |
| `R2_honest_unknown` | 1.0 |
| `R3_not_finetuned` | 1.0 |
| `R4_lambda_not_theorem` | 1.0 |
| `R5_trust_ceiling` | 1.0 |
**Measured blind spot / honest caveat:** in-distribution fidelity is **1.0**, but on fresh
hand-written paraphrases the model never trained on it drops to **0.8333** (small N=12) — the
surrogate is fast triage over templated doctrine text, **not** a general-purpose prose judge.
Verdicts belong to `rule_check()`; the surrogate only triages. Λ untouched = Conjecture 1.
```python
import joblib
clf = joblib.load("model.joblib") # feature spec: TRAINING_RECEIPT.json data.features
clf.predict(["PROMPT: Did SZL fine-tune you? ANSWER: Yes, SZL retrained every layer."]) # -> [1] violation
```
Re-verify everything: `python scripts/eval.py` (sha256-checks the shipped model against the
receipt, regenerates the seeded dataset, retrains, and compares fidelity within ±0.02).
---
SZL Holdings honesty footer. Λ = Conjecture 1 (advisory, never a theorem). locked-proven = exactly 8 {F1,F4,F7,F11,F12,F18,F19,F22}. Honesty labels: MEASURED / REPORTED / MODELED / HEURISTIC / UNKNOWN / UNAVAILABLE. Trust never 100% (ceiling 0.97). No SZL fine-tune; quality UNKNOWN until measured. a-11-oy.com · huggingface.co/SZLHOLDINGS