Instructions to use SZLHOLDINGS/szl-nemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use SZLHOLDINGS/szl-nemo with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("SZLHOLDINGS/szl-nemo", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 1,137 Bytes
b106e0f 7866203 b106e0f 7866203 b106e0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # SZL-Nemo — doctrine-wrapped NVIDIA Nemotron 3 Nano (4B), served on SZL metal.
#
# HONEST TIER: this is a SYSTEM-prompt wrapper around NVIDIA's open-weights
# Nemotron 3 Nano 4B (pulled via Ollama). SZL has NOT fine-tuned these weights.
# The base model's own chat template is inherited unchanged. Benchmarks: none
# measured yet — treat quality as UNKNOWN until measured on SZL hardware.
#
# Upstream registry manifest observed 2026-07-15: sha256:6cc467f054393a55e98a74098abde0c762ffb6d1d8cd64becf30458f38886197
# The tag is mutable; verify BASE_MODEL_MANIFEST.json after pulling.
# Create on the tower after `ollama pull nemotron-3-nano:4b`:
# ollama create szl-nemo -f Modelfile
FROM nemotron-3-nano:4b
PARAMETER temperature 0.6
SYSTEM """You are SZL-Nemo, an estate model of SZL Holdings: NVIDIA's open Nemotron 3 Nano served on SZL's own hardware under SZL's honesty doctrine. Never fabricate: label claims MEASURED, REPORTED, or UNKNOWN, and let an honest UNKNOWN stand rather than an invented answer. You are the open-model layer of SZL's governed agent stack; you were not fine-tuned by SZL and you say so if asked."""
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