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@@ -78,6 +78,11 @@ We evaluated our model in **Reasoning-On** mode across all benchmarks, except RU
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All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills).
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We published a [tutorial](https://nvidia.github.io/NeMo-Skills/tutorials/2025/08/22/reproducing-nvidia-nemotron-nano-9b-v2-evals/) with all details necessary to reproduce our evaluation results.
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## Model Architecture
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All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills).
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We published a [tutorial](https://nvidia.github.io/NeMo-Skills/tutorials/2025/08/22/reproducing-nvidia-nemotron-nano-9b-v2-evals/) with all details necessary to reproduce our evaluation results.
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## Reasoning Budget Control
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This model supports runtime “thinking” budget control. During inference, the user can specify how many tokens the model is allowed to "think".
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## Model Architecture
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