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  "text": "NVIDIA Nemotron-Nano v2\n\nModels: 12B Base, 9B Reasoning, 9B Base\n- Arch: Hybrid Mamba2–Transformer (128K ctx, 4 attn layers)\n- Training: 10.6T tokens (3.5T synthetic from DeepSeek, Qwen, Nemotron-4, phi-4, etc.)\n- 15 natural languages + 43 programming languages\n- Datasets: Nemotron-CC v2 + Nemotron-CC-Math (133B tokens, 5.5× FineMath)\n\nBenchmarks\n- Math: 91.4 GSM8K CoT, 63.6 MATH L5, +30→56.7 AIME\n- Code: 58.5 HumanEval+, 58.9 MBPP+\n- Commonsense: 90.7 ARC, 79.9 HellaSwag\n- Long-context: 82.2 RULER-128K\n\nHighlights \n- Nemotron-CC-Math: First scalable pipeline using Lynx + LLM cleanup to preserve LaTeX + code in web data. Delivers SOTA boosts (+12.6 MATH, +14.3 MBPP+) vs prior open math sets\n- Efficiency: Distilled 12B→9B (480B tokens), ~1.5e24 FLOPs, ~724 MWh disclosed\n- Deployment: Hugging Face, NGC, NeMo, TRT-LLM, vLLM | GPU-optimized\n- Open: Models, datasets, and full extraction pipelines released",
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