nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF · Hugging Face

Source: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF

**Model Developer:**NVIDIA Corporation

Model Dates:

Dec 2025 - Jan 2026

Data Freshness:

September 2024

The pretraining data has a cutoff date of September 2024.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#model-overviewModel Overview

NVIDIA-Nemotron-3-Nano-4B-GGUF is quantized version (Q4_K_M) of NVIDIA-Nemotron-3-Nano-4B-BF16 and is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model’s reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.

The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the NemotronElasticframework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers.

The supported languages include: English. Improved using Qwen.

This model is ready for commercial use.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#licenseterms-of-useLicense/Terms of Use

Governing Terms: Use of this model is governed by theNVIDIA Nemotron Open Model License.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#evaluation-resultsEvaluation Results:

We evaluated our model in **Reasoning-off** mode across these benchmarks

BenchmarkNVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA-Nemotron-3-Nano-4B-FP8NVIDIA-Nemotron-3-Nano-4B-Q4_K_MIFBench-Prompt43.243.8846.9IFBench-Instruction44.244.7849.6Orak22.920.7219.8IFEval-Prompt82.885.7781.5IFEval-Instruction88.087.5383.9HaluEval62.262.262.4RULER (128k)91.191.091.2 All evaluations were done usingNeMo-Skills&Orak. For Orak we evaluated on three games (Super Mario, Darkest Dungeon & StarDew Valley)

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#deployment-geography-globalDeployment Geography: Global

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#use-caseUse Case

NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#release-date-3162026Release Date: 3/16/2026

Huggingface 3/16/2026 viahttps://huggingface.co/

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#referencesReferences

• NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
• Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs
• NVIDIA Nemotron 3: Efficient and Open Intelligence
• Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
• Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#model-architectureModel Architecture

• Architecture Type: Mamba2-Transformer Hybrid
• Network Architecture: Nemotron-Hybrid- This model was compressed fromnvidia/NVIDIA-Nemotron-Nano-9B-v2 - Number of model parameters 3.97 x 10^9

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#inputInput

• Input Type(s): Text
• Input Format(s): String
• Input Parameters: One-Dimensional (1D): Sequences
• Other Properties Related to Input: Context length up to 262K. Supported languages include English.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#outputOutput

• Output Type(s): Text
• Output Format: String
• Output Parameters: One-Dimensional (1D): Sequences
• Other properties Related to Output: Sequences up to 262K

Our 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.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#software-integrationSoftware Integration

• Runtime Engine(s): NeMo 25.07
• Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100, GeForce RTX
• 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.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#use-it-with-llamacppUse it with llama.cpp

Launch OpenAI-compatible API server

./llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF:Q4_K_M -c 0 --alias my_model -ngl 999 --port 5000 --host 0.0.0.0

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#model-versionModel Version

• v1.0

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#training-testing-and-evaluation-datasetsTraining, Testing, and Evaluation Datasets

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#training-datasetsTraining datasets

• Data Modality: Text
• Text Training Data Size: More than 10 Trillion Tokens
• Train/Test/Valid Split: We used 100% of the corpus for pre-training and relied on external benchmarks for testing.
• Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
• Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

**Properties:**The post-training corpus for NVIDIA-Nemotron-3-Nano-4B consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English). Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including code, legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies. For several of the domains listed above we used synthetic data, specifically reasoning traces, from DeepSeek R1/R1-0528, Qwen3-235B-A22B, Nemotron 4 340B, Qwen2.5-32B-Instruct-AWQ, Qwen2.5-14B-Instruct, Qwen 2.5 72B.

More details on the datasets and synthetic data generation methods can be found in the technical reportNVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#public-datasetsPublic Datasets

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#private-non-publicly-accessible-datasets-of-third-partiesPrivate Non-publicly Accessible Datasets of Third Parties

DatasetGlobal RegulationWorkbench

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#online-dataset-sourcesOnline Dataset Sources

The English Common Crawl data was downloaded from the Common Crawl Foundation (see theirFAQfor 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 theNemotron-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).

DatasetModalityDataset Size (Tokens)Collection PeriodEnglish Common CrawlText3.360T4/8/2025Multilingual Common CrawlText812.7B5/1/2025GitHub CrawlText747.4B4/29/2025English Common Crawl 1.1TextNot disclosed10/2/2025

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#nvidia-sourced-synthetic-datasetsNVIDIA-Sourced Synthetic Datasets

DatasetModalityDataset Size (Tokens)Seed DatasetModel(s) used for generationSynthetic Art of Problem Solving from DeepSeek-R1Text25.5BArt of Problem Solving;American Mathematics Competitions 8;American Mathematics Competitions 10;DeepSeek-R1Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1Text327Msocial-chemestry-101;Moral StoriesMixtral-8x22B-v0.1Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72BText83.6MOpenStax - CC BY-SA subsetDeepSeek-V3;Mixtral-8x22B-v0.1;Qwen2.5-72BSynthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72BText9.7MOpenStax - CC BY-SA subsetDeepSeek-V3;Mixtral-8x22B-v0.1;Qwen2.5-72BSynthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72BText175MOpenStax - CC BY-SA subset;GSM8K;Open Textbook Library - CC BY-SA & GNU subsetDeepSeek-R1,DeepSeek-V3;DeepSeek-V3-0324;Qwen2.5-72BNemotron-PrismMathText4.6BBig-Math-RL-Verified;OpenR1-Math-220kQwen2.5-0.5B-instruct,Qwen2.5-72B-Instruct;DeepSeek-R1-Distill-Qwen-32BSynthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-InstructText350MarXiv;National Institutes of Health ExPorter;BioRxiv;PMC Article;USPTO Backgrounds;peS2o; Global Regulation;CORE;PG-19;DOAB CC BY & CC BY-SA subset;NDLTDQwen2.5-72B-InstructSynthetic FineMath-4+ Reprocessed from DeepSeek-V3Text9.2BCommon CrawlDeepSeek-V3Synthetic FineMath-3+ Reprocessed from phi-4Text27.6BCommon Crawlphi-4Synthetic Union-3+ Reprocessed from phi-4Text93.1BCommon Crawlphi-4RefreshedNemotron-MINDfrom phi-4Text73BCommon Crawlphi-4Synthetic Union-4+ Reprocessed from phi-4Text14.12BCommon Crawlphi-4Synthetic Union-3+ minus 4+ Reprocessed from phi-4Text78.95BCommon Crawlphi-4Synthetic Union-3 Refreshed from phi-4Text80.94BCommon Crawlphi-4Synthetic Union-4+ Refreshed from phi-4Text52.32BCommon Crawlphi-4Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324Text4.0BAQUA-RAT;LogiQA;AR-LSATDeepSeek-V3;DeepSeek-V3-0324Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3BText4.2BAQUA-RAT;LogiQA;AR-LSATQwen3-30B-A3BSynthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-InstructText83.1BArt of Problem Solving;American Mathematics Competitions 8;American Mathematics Competitions 10;GSM8K;PRM800KQwen2.5-32B-Instruct;Qwen2.5-Math-72B;Qwen2.5-Math-7B;Qwen2.5-72B-InstructSynthetic MMLU Auxiliary Train from DeepSeek-R1Text0.5BMMLU Auxiliary TrainDeepSeek-R1Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-InstructText5.4BarXiv;National Institutes of Health ExPorter;BioRxiv;PMC Article;USPTO Backgrounds;peS2o; Global Regulation;CORE;PG-19;DOAB CC BY & CC BY-SA subset;NDLTDQwen2.5-72B-InstructSynthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-InstructText1.949TCommon CrawlQwen3-30B-A3B;Mistral-NeMo-12B-InstructSynthetic Multilingual Data from Common Crawl from Qwen3-30B-A3BText997.3BCommon CrawlQwen3-30B-A3BSynthetic Multilingual Data from Wikimedia from Qwen3-30B-A3BText55.1BWikimediaQwen3-30B-A3BSynthetic OpenMathReasoning from DeepSeek-R1-0528Text1.5MOpenMathReasoningDeepSeek-R1-0528Synthetic OpenCodeReasoning from DeepSeek-R1-0528Text1.1MOpenCodeReasoningDeepSeek-R1-0528Synthetic Science Data from DeepSeek-R1-0528Text1.5M-DeepSeek-R1-0528Synthetic Humanity’s Last Exam from DeepSeek-R1-0528Text460KHumanity’s Last ExamDeepSeek-R1-0528Synthetic ToolBench from Qwen3-235B-A22BText400KToolBenchQwen3-235B-A22BSynthetic Nemotron Content Safety Dataset V2, eval-safety, Gretel Synthetic Safety Alignment, and RedTeam_2K from DeepSeek-R1-0528Text52KNemotron Content Safety Dataset V2;eval-safety;Gretel Synthetic Safety Alignment;RedTeam_2KDeepSeek-R1-0528Synthetic HelpSteer from Qwen3-235B-A22BText120KHelpSteer3;HelpSteer2Qwen3-235B-A22BSynthetic Alignment data from Mixtral-8x22B-Instruct-v0.1, Mixtral-8x7B-Instruct-v0.1, and Nemotron-4 FamilyText400KHelpSteer2;C4;LMSYS-Chat-1M;ShareGPT52K;tigerbot-kaggle-leetcodesolutions-en-2k;GSM8K;PRM800K; lm_identity (NVIDIA internal);FinQA;WikiTableQuestions;Riddles; ChatQA nvolve-multiturn (NVIDIA internal);glaive-function-calling-v2;SciBench;OpenBookQA;Advanced Reasoning Benchmark;Public Software Heritage S3;Khan Academy Math KeywordsNemotron-4-15B-Base (NVIDIA internal); Nemotron-4-15B-Instruct (NVIDIA internal);Nemotron-4-340B-Base;Nemotron-4-340B-Instruct;Nemotron-4-340B-Reward;Mixtral-8x7B-Instruct-v0.1;Mixtral-8x22B-Instruct-v0.1Synthetic LMSYS-Chat-1M from Qwen3-235B-A22BText1MLMSYS-Chat-1MQwen3-235B-A22BSynthetic Multilingual Reasoning data from DeepSeek-R1-0528, Qwen2.5-32B-Instruct-AWQ, and Qwen2.5-14B-InstructText25MOpenMathReasoning;OpenCodeReasoningDeepSeek-R1-0528;Qwen2.5-32B-Instruct-AWQ(translation);Qwen2.5-14B-Instruct(translation);Synthetic Multilingual Reasoning data from Qwen3-235B-A22B and Gemma 3 Post-Trained modelsText5MWildChatQwen3-235B-A22B;Gemma 3 PT 12B;Gemma 3 PT 27BTool Calling DataText26.2BQwen3-235B-A22B-2507;gpt-oss-120bSynthetic Essential-Web from QwQ-32BText28.1BEssential-WebQwQ-32BTranslated Synthetic CrawlText389.9BCommon CrawlQwen3-30B-A3BTranslated Synthetic WikipediaText7.9BWikimediaQwen3-30B-A3BSynthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-InstructTextUndisclosedArt of Problem Solving;American Mathematics Competitions 8;American Mathematics Competitions 10gpt-oss-120b;Qwen2.5-32B-InstructSynthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-InstructTextUndisclosedStack Exchangegpt-oss-120b;Qwen2.5-32B-InstructSynthetic OpenCodeReasoning from DeepSeek-R1-0528TextUndisclosedOpenCodeReasoningDeepSeek-R1-0528Synthetic HackerRank Coding from DeepSeek-R1-0528TextUndisclosedHackerRank Coding DatasetDeepSeek-R1-0528Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-InstructTextUndisclosedSWE-GymQwen3-Coder-480B-A35B-InstructSynthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32BTextUndisclosedArt of Problem Solving;American Mathematics Competitions 8;American Mathematics Competitions 10;Stack Exchangegpt-oss-120b;Qwen2.5-32B-Instruct;Goedel-Prover-V2-32BSynthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-InstructTextUndisclosedStack Exchange;SCP-116K;LIMO;TACO; Code Contest; CodeforcesDeepSeek-R1;DeepSeek-R1-0528;Qwen2.5-32B-Instruct;Qwen3-235B-A22B;Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b and Mixtral-8x7B-v0.1TextUndisclosedNemotron Content Safety Dataset V2;Gretel Synthetic Safety Alignment Dataset;RedTeam-2K;Malicious Tasks;Nemotron-Personas-USADeepSeek-R1-0528;gpt-oss-120b;Mixtral-8x7B-v0.1Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120bTextUndisclosedarXiv;National Institutes of Health ExPorter;BioRxiv;PMC Article;USPTO Backgrounds;peS2o; Global Regulation;CORE;PG-19;DOAB CC BY & CC BY-SA subset;NDLTDQwen3-235B-A22B-Instruct-2507;gpt-oss-120bSynthetic KernelBook from DeepSeek-R1-0528TextUndisclosedKernelBookDeepSeek-R1-0528Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-ThinkingTextUndisclosedToolBench;glaive-function-calling-v2;APIGen Function-Calling;Nemotron-Personas-USAQwen3-235B-A22B-Thinking-2507;Qwen3-Next-80B-A3B-ThinkingSynthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507 , and Qwen3-235B-A22B-Thinking-2507TextUndisclosedC4;LMSYS-Chat-1M;ShareGPT;GSM8K;PRM800K;FinQA;WikiTableQuestions;Riddles;glaive-function-calling-v2;SciBench;tigerbot-kaggle-leetcodesolutions-en-2k;OpenBookQA;Advanced Reasoning Benchmark; Software Heritage;Khan Academy Math Keywords;WildChat-1M;Nemotron-Personas-USAgpt-oss-120b;Mixtral-8x22B-Instruct-v0.1;Qwen3-235B-A22B-Instruct-2507;Qwen3-235B-A22B-Thinking-2507Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507TextUndisclosedCORE;PG-19;DOAB CC BY & CC BY-SA subset;NDLTDQwen3-235B-A22B-Instruct-2507Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507TextUndisclosedNVIDIA Internalgpt-oss-120b;DeepSeek-R1-0528;Qwen3-32B; andQwen3-235B-A22B-Thinking-2507Synthetic STEM from Qwen3-235B-A22B-Thinking-2507TextUndisclosedICHO-IPH0;Physics Big; Scale HLE;OpenMathReasoning;OpenCodeReasoningQwen3-235B-A22B-Thinking-2507Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-ThinkingTextUndisclosedDocFinQA;SWE-smithQwen3-Coder-480B-A35B-Instruct;Kimi-K2-ThinkingSynthetic Math from gpt-oss-120b and Qwen2.5-32B-InstructTextUndisclosed-gpt-oss-120b;Qwen2.5-32B-InstructSynthetic Essential-Web from gpt-oss-120bTextUndisclosedEssential-Webgpt-oss-120bSynthetic Scale HLE from gpt-oss-120bTextUndisclosedScale HLEgpt-oss-120bSynthetic CDQuestions from gpt-oss-120bTextUndisclosedCDQuestionsgpt-oss-120bSynthetic Stack Exchange from gpt-oss-120bTextUndisclosedStack Exchangegpt-oss-120bSynthetic GPQA from gpt-oss-120b and Qwen2.5-32B-InstructTextUndisclosedStack Exchangegpt-oss-120b;Qwen2.5-32B-InstructSynthetic Vedantu from gpt-oss-120bTextUndisclosedVedantugpt-oss-120bSynthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-InstructTextUndisclosedSWE-Gym;R2E-Gym-SubsetQwen3-Coder-480B-A35B-InstructSynthetic SWE-Gym from Qwen3-Coder-480B-A35B-InstructTextUndisclosedSWE-GymQwen3-Coder-480B-A35B-InstructSynthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528TextUndisclosedSWE-Gym;R2E-Gym-SubsetDeepSeek-R1-0528Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507TextUndisclosedHelpSteer2;HelpSteer3;LMSYS-Chat-1M;Nemotron-Personas-USAgpt-oss-120b;Qwen3-235B-A22B-Instruct-2507;Qwen3-235B-A22B-Thinking-2507Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507TextUndisclosed-Qwen3-30B-A3B-Instruct-2507;Qwen3-30B-A3B-Thinking-2507;Qwen3-235B-A22B-Instruct-2507;Qwen3-235B-A22B-Thinking-2507Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528TextUndisclosed-Qwen3-235B-A22B;DeepSeek-R1-0528Synthetic Search STEM OPENQ from DeepSeek-R1-0528TextUndisclosed-DeepSeek-R1-0528Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528TextUndisclosed-Qwen2.5-32B-Instruct;DeepSeek-R1-0528Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528TextUndisclosed-Qwen2.5-32B-Instruct;DeepSeek-R1-0528Synthetic MCQ10 from DeepSeek-R1-0528TextUndisclosed-DeepSeek-R1-0528Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507TextUndisclosed-Qwen3-235B-A22B;DeepSeek-R1-0528;Qwen3-235B-A22B-Instruct-2507Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-InstructTextUndisclosedOpenMathReasoninggpt-oss-120b;Qwen2.5-32B-InstructSynthetic Offline Search MCQA HLE from DeepSeek-R1-0528TextUndisclosed-DeepSeek-R1-0528Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528TextUndisclosed-Qwen3-235B-A22B;DeepSeek-R1-0528Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528TextUndisclosed-QwQ-32B;Qwen3-30B-A3B;Qwen3-235B-A22B;Qwen3-235B-A22B-Instruct-2507;Mistral-Small-3.1-24B-Instruct-2503;Mistral-Small-3.2-24B-Instruct-2506;MiniMax-M1-80k;MiniMax-M1-40k;Kimi-K2-Instruct;DeepSeek-V3-0324;DeepSeek-R1-0528Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32BTextUndisclosedWildChat-1M;arena-human-preference-140kDeepSeek-R1;gemma-2-2b-it;gemma-3-27b-it;gpt-oss-20b;gpt-oss-120b;Mistral-7B-Instruct-v0.3;Mixtral-8x22B-Instruct-v0.1;Nemotron-4-340B-Instruct;NVIDIA-Nemotron-Nano-9B-v2;Phi-4-mini-instruct;Phi-3-small-8k-instruct;Phi-3-medium-4k-instruct;Qwen3-235B-A22B;QwQ-32BSynthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1TextUndisclosedNemotron Content Safety Dataset V2;Gretel Synthetic Safety Alignment Dataset;RedTeam-2K;Malicious Tasks;DeepSeek-R1-0528;gpt-oss-120b;DeepSeek-R1-Distill-Qwen-7B;Qwen3-30B-A3B-Thinking-2507;Qwen3-235B-A22B-Instruct-2507;Mixtral-8x7B-v0.1Synthetic Code from Qwen3-32BTextUndisclosedEnglish Common Crawl; English Common Crawl 1.1Qwen3-32BSynthetic OpenCodeReasoning from DeepSeek-R1TextUndisclosedOpenCodeReasoningDeepSeek-R1Synthetic LIMO from DeepSeek-R1-0528TextUndisclosedLIMODeepSeek-R1-0528Synthetic SCP from DeepSeek-R1-0528TextUndisclosedSCP-116KDeepSeek-R1-0528Synthetic Stack Exchange from DeepSeek-R1-0528TextUndisclosedStack ExchangeDeepSeek-R1-0528Synthetic Common Crawl from Qwen3-30B-A3BTextUndisclosedCommon CrawlQwen3-30B-A3BSynthetic Wikipedia from Qwen3-30B-A3BTextUndisclosedWikimediaQwen3-30B-A3BSynthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507TextUndisclosedEssential-WebQwen3-30B-A3B;Qwen3-235B-A22B-Thinking-2507Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4TextUndisclosedCommon Crawl;FineMathQwen3-30B-A3B;Qwen3-235B-A22B;phi-4Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528TextUndisclosedMagicoder-Evol-Instruct-110K;opc-sft-stage2;TACO;OpenCodeReasoning;OpenMathReasoning;NuminaMath CoTDeepSeek-R1;DeepSeek-R1-0528Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8BTextUndisclosedNemotron-Personas-USAgpt-oss-120b;Qwen3-8B

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#-1

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#-2

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#evaluation-datasetEvaluation Dataset:

• Data Collection Method by dataset: Hybrid: Human, Synthetic
• Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#inferenceInference

• Engines: HF, vLLM, llama-cpp, TRT-LLM, SGLang
• Test Hardware: NVIDIA GeForce RTX, H100 80GB, DGX Spark, Jetson Thor/Orin Nano.

https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF#ethical-considerationsEthical 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 ourTrustworthy AI terms of service, 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.

We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case.For more details:SafetyandExplainabilitySubcards.

For more detailed information on ethical considerations for this model, please see the Model Card++Bias, andPrivacySubcards.

Please report security vulnerabilities or NVIDIA AI Concernshere.