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README.md
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library_name: transformers
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tags:
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# NVIDIA-Nemotron-Nano-9B-v2
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**Model Developer:** NVIDIA Corporation
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**Model Dates:**
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This model is ready for commercial use.
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## Evaluation Results
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We evaluated our model in \*\*Reasoning-On\*\* mode across all benchmarks.
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All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills/tree/main/docs).
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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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## License/Terms of Use
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GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
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## Model Architecture
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- [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf)
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## Computational Load
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Cumulative compute : 1.53E+24 FLOPS
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- ja
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library_name: transformers
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tags:
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- nvidia
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- pytorch
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---
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# NVIDIA-Nemotron-Nano-9B-v2
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**Model Developer:** NVIDIA Corporation
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**Model Dates:**
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This model is ready for commercial use.
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## License/Terms of Use
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GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
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## Evaluation Results
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### Benchmark Results (Reasoning On)
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We evaluated our model in \*\*Reasoning-On\*\* mode across all benchmarks.
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| Benchmark | Qwen3-8B | NVIDIA-Nemotron-Nano-9B-v2 |
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| :---- | ----: | ----: |
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| AIME25 | 69.3% | 72.1% |
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| MATH500 | 96.3% | 97.8% |
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| GPQA | 59.6% | 64.0% |
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| LCB | 59.5% | 71.1% |
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| BFCL v3 | 66.3% | 66.9% |
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| IFEval (Instruction Strict) | 89.4% | 90.3% |
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| HLE | 4.4% | 6.5% |
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| RULER (128K) | 74.1% | 78.9% |
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All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills/tree/main/docs).
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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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- [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf)
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## Computational Load
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Cumulative compute : 1.53E+24 FLOPS
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