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README.md

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+# NVIDIA-Nemotron-Nano-9B-v2-Base
+
+**Model Developer:** NVIDIA Corporation
+
+**Model Dates:**
+
+June 2025 \- August 2025
+
+**Data Freshness:**
+
+May 1, 2025
+
+The pretraining data has a cutoff date of May 1, 2025\.
+
+## Model Overview
+
+## Description
+
+NVIDIA-Nemotron-Nano-9B-v2-Base is a large language model (LLM) trained from scratch by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just four Attention layers. The model features a context length of 128K. The supported languages include: English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, and Thai.
+
+This model is ready for commercial/non-commercial use. 
+
+## License/Terms of Use 
+
+GA: GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0).
+
+### Deployment Geography: Global
+
+### Use Case
+
+This model is intended for developers and researchers building LLMs. 
+
+### Release Date: 08/15/2025
+
+Huggingface 08/15/2025 via [https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2-Base](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2-Base) 
+
+## Reference(s)
+
+[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) 
+
+## Model Architecture
+
+- **Architecture Type:** Mamba2-Transformer Hybrid    
+    
+- **Network Architecture:** Nemotron-Hybrid   
+    
+- **Number of model parameters:** *8.89B*
+
+
+This model was developed based on [NVIDIA-Nemotron-Nano-12B-v2-Base](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base). 
+
+## Model design
+
+*The model was pruned and distilled from [NVIDIA-Nemotron-Nano-12B-v2-Base](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base) with \~480B tokens.*
+
+## Computational load
+
+Cumulative compute : 1.48E+24 FLOPS
+
+Estimate energy and emissions for model training: 724.0 MWh
+
+|  | \# of tokens | Compute \[FLOPS\] | Energy \[MWh\] |
+| :---- | :---- | :---- | :---- |
+| 12B Base Pre-training | 20T | 1.45E+24 | 708.3 |
+| 9B Pruning & Distillation | 480B | 3.28E+22 | 15.7 |
+| Total | 20.6T | 1.48E+24 | 724.0 |
+
+## Input
+
+- **Input Type(s):** Text   
+    
+- **Input Format(s):** String   
+    
+- **Input Parameters:** One-Dimensional (1D): Sequences   
+    
+- **Maximum input size:** 128K tokens   
+    
+- **Other Properties Related to Input:** Supported languages include English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, Thai. 
+
+
+## Output
+
+- **Output Type(s):** Text   
+    
+- **Output Format:** String   
+    
+- **Output Parameters:** One-Dimensional (1D): Sequences   
+    
+- **Maximum output size:** 128K tokens 
+
+
+Our AI models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Software Integration
+
+- Runtime Engine(s): NeMo 25.07.nemotron-nano-v2  
+- Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100  
+- Operating System(s): Linux
+
+
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
+
+## Model Version(s)
+
+- v1.0
+
+# Training, Testing, and Evaluation Datasets:
+
+NVIDIA-Nemotron-Nano-9B-v2-Base is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately twenty trillion tokens.
+
+**Data Modality:**  Text **The total size:**  10,648,823,153,919 Tokens **Total number of datasets:** 141 **Dataset partition:** *Training \[100%\], testing \[0%\], validation \[0%\]*  
+**Time period for training data collection:** 2013 to May 1, 2025  
+**Time period for testing data collection:** 2013 to May 1, 2025  
+**Time period for validation data collection:** 2013 to May 1, 2025
+
+More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model.
+
+| Dataset | Collection Period |
+| :---- | :---- |
+| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
+| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |
+| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |
+| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |
+| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |
+| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |
+| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |
+| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |
+| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |
+| [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax \- CC BY-SA subset](https://openstax.org/) | 4/23/2025 |
+| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |
+| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |
+| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |
+| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |
+| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |
+| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |
+| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |
+| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |
+| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |
+| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |
+| [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |
+| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |
+| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |
+| [Reddit Dataset](https://files.pushshift.io/reddit/) | Legacy Download |
+| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |
+| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |
+| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |
+| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |
+| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |
+| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |
+| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |
+| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |
+| [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |
+| [FinQA](https://finqasite.github.io/) | Legacy Download |
+| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |
+| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |
+| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |
+| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |
+| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |
+| [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |
+| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |
+| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |
+| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |
+| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |
+| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |
+| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |
+| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |
+
+## Private Non-publicly Accessible Datasets of Third Parties
+
+| Dataset |
+| :---- |
+| Global Regulation |
+
+## Crawled and Scraped from Online Sources by NVIDIA
+
+The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.
+
+The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report). 
+
+| Dataset | Modality | Dataset Size | Collection Period | Collecting Organisation |
+| :---- | :---- | :---- | :---- | :---- |
+| English Common Crawl | Text | 3.36T | 4/8/2025 | NVIDIA Advanced Deep Learning Research |
+| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 | NVIDIA Advanced Deep Learning Research |
+| GitHub Crawl | Text | 747.4B | 4/29/2025 | NVIDIA Advanced Deep Learning Research |
+
+## NVIDIA-Sourced Synthetic Datasets
+
+| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |
+| :---- | :---- | :---- | :---- | :---- |
+| Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 40086030608 | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
+| Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |
+| Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 83.6M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 9.7M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B | Text | 175M | [OpenStax \- CC BY-SA subset](https://openstax.org/); [GSM8K](https://github.com/openai/grade-school-math); [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| [Nemotron-PrismMath](https://huggingface.co/datasets/nvidia/Nemotron-PrismMath) | Text | 4.6B | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified); [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | [Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct); [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |
+| Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Refreshed [Nemotron-MIND](https://huggingface.co/datasets/nvidia/Nemotron-MIND) from phi-4 | Text | 73B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| nv-cc-math-45-jun2025 | Text | 52.3B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3) |
+| nv-cc-math-3-jun2025 | Text | 80.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |
+| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text |  | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k) | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B); [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B); [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
+| Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text |  | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 415.8B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) |
+| Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text |  | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text |  | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text |  | \- | [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) |
+
+## Training Dataset :
+
+| Dataset | \# Tokens |
+| :---- | :---- |
+| English Common Crawl | 3,360,110,334,818 |
+| English Synthetic CC | 1,949,464,641,123 |
+| Crawl++ | 360,389,153,262 |
+| Math | 124,606,230,663 |
+| Synthetic Math | 73,007,767,155 |
+| Code | 747,409,228,724 |
+| Synthetic Code | 175,067,553,293 |
+| English Wiki | 17,349,266,926 |
+| Books | 0 |
+| Papers | 191,586,493,365 |
+| PDF-to-text | 141,096,578,533 |
+| Code SFT | 60,025,726,817 |
+| STEM SFT | 272,680,426,295 |
+| General SFT | 6,057,478,645 |
+| Multilingual | 2,172,261,909,350 |
+| Synthetic multilingual | 997,710,364,950 |
+| Total | 10,648,823,153,919 |
+
+We use a considerable amount of synthetic data. Out of 10.6 trillion tokens, 3,534,013,958,278 tokens are synthetically generated.
+
+We extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC. Additionally, we used data from Wikipedia and FineWeb-2 (Penedo et al., 2025\) for these fifteen languages.
+
+| Language | Total Tokens |
+| :---- | :---- |
+| Arabic | 118,056,362,726 |
+| Danish | 117,747,321,618 |
+| German | 146,613,691,781 |
+| Spanish | 469,156,575,409 |
+| French | 139,982,002,289 |
+| Italian | 298,858,370,174 |
+| Japanese | 682,755,693,336 |
+| Korean | 127,099,747,538 |
+| Dutch | 89,041,592,681 |
+| Polish | 105,356,493,147 |
+| Portuguese | 243,249,275,089 |
+| Russian | 185,314,014,057 |
+| Swedish | 74,954,953,299 |
+| Thai | 160,778,944,467 |
+| Chinese | 211,007,236,689 |
+
+We collect a total of 922,476,782,017 tokens of code in 43 different languages.
+
+| Language | Tokens |
+| :---- | :---- |
+| Assembly | 750,628,764 |
+| C | 42,657,300,868 |
+| C\# | 56,153,329,307 |
+| C++ | 67,773,701,658 |
+| CommonLisp | 263,234,672 |
+| CSS | 38,848,760,035 |
+| Cuda | 400,222,993 |
+| Dart | 3,816,960,470 |
+| Dockerfile | 474,958,084 |
+| Fortran | 1,105,049,387 |
+| Go | 8,332,419,480 |
+| Haskell | 1,294,613,669 |
+| HTML | 69,082,117,487 |
+| Java | 131,440,465,822 |
+| JavaScript | 75,573,420,861 |
+| JSON | 15,366,881,241 |
+| Julia | 621,046,949 |
+| JupyterNotebook | 2,241,893,197 |
+| Lua | 4,146,420,802 |
+| Makefile | 12,640,010,879 |
+| Markdown | 64,796,743,311 |
+| Mathematica | 320,504,225 |
+| OmniversePython | 26,946,093 |
+| Pascal | 1,625,013,876 |
+| Perl | 1,575,314,434 |
+| PHP | 61,575,339,005 |
+| Python | 126,916,727,384 |
+| R | 19,811,381,935 |
+| reStructuredText | 1,779,876,391 |
+| Ruby | 6,446,962,615 |
+| Rust | 4,438,640,533 |
+| Scala | 3,343,959,154 |
+| Shell | 18,758,779,250 |
+| SQL | 23,205,633,085 |
+| Swift | 5,976,714,881 |
+| SystemVerilog | 233,056,185 |
+| TeX | 7,347,157,527 |
+| TypeScript | 15,657,838,582 |
+| Verilog | 811,884,369 |
+| VHDL | 648,401,444 |
+| VisualBasic.NET | 1,005,680,881 |
+| XML | 12,616,779,741 |
+| YAML | 10,574,010,491 |
+
+## 
+
+## Evaluation Dataset:
+
+
+* Data Collection Method by dataset: Hybrid: Human, Synthetic  
+* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
+
+
+### Base Benchmark Evaluations
+
+We evaluated our model on the following benchmarks:
+
+| Task | N-Nano-V2 12B Base |  | N-Nano-V2 9B Base | Qwen3 8B Base | Gemma3 12B Base |
+| :---- | :---- | :---- | :---- | :---- | :---- |
+| **General** |  |  |  |  |  |
+| MMLU | 78.24 |  | 74.53 | 76.44 | 73.61 |
+| MMLU-Pro 5-shot | 63.98 |  | **59.43** | 56.27 | 45.12 |
+| AGIEval English CoT | 68.03 |  | **65.28** | 59.54 | 51.69 |
+| Math |  |  |  |  |  |
+| GSM8K CoT | 91.66 |  | **91.36** | 84.00 | 74.45 |
+| **Math** | 83.54 |  | **80.50** | 55.40 | 42.40 |
+| MATH Level 5 | 67.61 |  | **63.64** | 29.91 | 17.71 |
+| AIME 2024 avg@32 | 56.67 |  | 30.00 | 20.00 | 16.67 |
+| **Code** |  |  |  |  |  |
+| HumanEval+ Pass@1 | 61.03 |  | 58.50 | 57.55 | 36.68 |
+| MBPP+ Pass@1 | 61.55 |  | 58.95 | 58.56 | 51.73 |
+| **Commonsense Understanding** |  |  |  |  |  |
+| ARC Challenge | 93.26 |  | 90.70 | **93.09** | 90.44 |
+| HellaSwag | 84.00 |  | 79.90 | 79.75 | **84.15** |
+| OpenBookQA | 46.00 |  | 44.80 | 42.00 | **46.00** |
+| PIQA | 82.54 |  | 81.83 | 79.43 | **82.10** |
+| WinoGrande | 79.24 |  | 75.30 | 75.93 | **79.95** |
+| **Long Context** |  |  |  |  |  |
+| RULER-128K | 84.74 |  | **82.22** | \- | 80.70 |
+
+*Table 1: Accuracy of Nemotron-Nano-V2-Base models versus existing SoTA models. N-Nano-V2 is short for Nemotron-Nano-V2. The distilled N-Nano-V2-9B-Base is compared against Qwen3-8B-Base and Gemma3-12B-Base, and the best score is highlighted in each row.*
+
+| Task | N-Nano-V2 12B Base |  | N-Nano-V2 9B Base | Qwen3 8B Base | Gemma3 12B Base |
+| :---- | :---- | :---- | :---- | :---- | :---- |
+| **Global-MMLU-Lite** |  |  |  |  |  |
+| German | 74.50 |  | 68.25 | **75.50** | 69.75 |
+| Spanish | 76.50 |  | 72.75 | **75.00** | 74.00 |
+| French | 78.25 |  | 69.75 | **74.25** | 72.50 |
+| Italian | 76.50 |  | 73.25 | 72.75 | **74.00** |
+| Japanese | 71.00 |  | 67.00 | 70.00 | **71.50** |
+| Korean | 72.50 |  | 67.25 | 67.25 | **70.25** |
+| Portuguese | 76.25 |  | 71.25 | 72.50 | **75.75** |
+| Chinese | 75.50 |  | 69.25 | **75.25** | 67.25 |
+| Average | 75.13 |  | 69.94 | **72.81** | 71.88 |
+| **Multilingual Math (MGSM)** |  |  |  |  |  |
+| Spanish | 93.20 |  | **91.60** | 86.40 | 74.00 |
+| German | 89.60 |  | **89.60** | 78.80 | 68.80 |
+| French | 86.40 |  | **86.00** | 78.80 | 70.80 |
+| Chinese | 44.40 |  | **75.20** | 28.80 | 26.80 |
+| Japanese | 76.00 |  | **74.80** | 30.80 | 26.40 |
+| Russian | 90.40 |  | **91.60** | 83.60 | 76.00 |
+| Average | 80.00 |  | **84.80** | 64.53 | 57.13 |
+
+*Table 2: Accuracy of Nemotron-Nano-V2-Base models versus existing SoTA models on multilingual benchmarks. N-Nano-V2 is short for Nemotron-Nano-V2. The distilled N-Nano-V2-9B-Base is compared against Qwen3-8B-Base and Gemma3-12B-Base, and the best score is highlighted in each row.*
+
+## Inference
+
+- ## Engines: HF, vLLM, TRT-LLM
+
+- ## Test Hardware NVIDIA A10G 24GB, H100 80GB
+
+## Ethical Considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our [Trustworthy AI terms of service](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Ftrustworthy-ai%2Fterms%2F&data=05%7C02%7Cbsimkin%40nvidia.com%7Cdb502dd5d52f4a725b6e08ddd9be6f84%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906134164797524%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=2VL7oGfJbFtEnSoL4deMIQVwOCzzZ8bfm0wUDGjazk0%3D&reserved=0), developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](http://./bias.md), [Explainability](http://./explainability.md), [Safety & Security](http://./safety.md), and [Privacy](http://./privacy.md) Subcards.
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+# Subcards:
+
+## Bias
+
+| Field | Response |
+| :---- | :---- |
+| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |
+| Bias Metric (If Measured): | Not Available  |
+| Which characteristic (feature) show(s) the greatest difference in performance?: | The model shows high variance in the characteristics when it is used with a high temperature. |
+| Which feature(s) have the worst performance overall? | Not Available  |
+| Measures taken to mitigate against unwanted bias: | Not Available  |
+| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | The training datasets contain a large amount of synthetic data generated by LLMs. We manually curated prompts. |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | Not Available  |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as Common Crawl, CC-News, and Wikimedia, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in over 85% of samples. In the subset where such terms are present, Common Crawl and CC-News contain notable representational skews—for example, references to "male" significantly outnumber those to "female," and mentions of "White" are the most frequent among ethnic identifiers. To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies like counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy, and includes outputs from uncalibrated embedders; as such, certain limitations may exist in the reliability of the embedding. |
+
+# 
+
+## Explainability
+
+| Field | Response |
+| :---- | :---- |
+| Intended Task/Domain: | Text generation, reasoning, and chat |
+| Model Type: | Text-to-text Mamba2-Transformer Hybrid |
+| Intended Users: | Generative AI creators working with conversational AI models and image content. |
+| Output: | Text |
+| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | We used a Gemma-3 4B-based filtering model fine-tuned on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) to ensure the quality of synthetic data. |
+| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |
+| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |
+| Technical Limitations & Mitigation: | The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs. The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. |
+| Verified to have met prescribed NVIDIA quality standards: | Yes |
+| Performance Metrics: | Accuracy, Throughput, and User-side throughput |
+| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources  \-- either directly or indirectly by retrieval (e.g. via visiting a website)  \-- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place. The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
+| Licensing: | GA: GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0). |
+
+## Privacy
+
+| Field | Response |
+| :---- | :---- |
+| Generatable or reverse engineerable personal data? | No |
+| Personal data used to create this model? | No |
+| Was consent obtained for any personal data used? | Not Applicable |
+| A description of any methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data, where relevant and applicable. | We used only prompts that do not contain any personal data for synthetic data generation. |
+| How often is the dataset reviewed? | Before Release |
+| Is there provenance for all datasets used in training? | Yes |
+| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
+| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |
+| Applicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |
+| During AI model development, strict adherence to copyright policy ensured compliance through risk mitigation and legal reviews. Post-data collection, reserved rights content is identified and removed, with verified opt-out processes for rightsholders. Detailed records document due diligence and transparency. | True |
+| We employ automated tools and data processing techniques during pre-training to identify and filter certain categories of personal information. Scans of training datasets detected no PII. | True. We employ automated tools and data processing techniques to scan for Personally Identifiable Information (PII) during pre-training to identify and filter certain categories of personal information, including public-facing contact details such as email addresses and phone numbers. Scans of Common Crawl, CC-News, and Wikimedia datasets did not detect PII in the majority of samples. However, Microsoft Presidio indicated potential findings including business contact information embedded in natural language, such as email addresses and phone numbers. These were removed using verified instances of PII through a combination of automated filtering and human-in-the-loop validation. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.  |
+
+## Safety & Security
+
+| Field | Response |
+| :---- | :---- |
+| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |
+| Describe the life critical impact (if present). | Not Applicable |
+| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from  training.  |
+| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |
+| Use Case Restrictions: | GA: Abide by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0). |
+| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
+| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |
+