datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nemotron-Terminal-Corpus
Terminal-Corpus: Large-Scale SFT Dataset for Terminal Agents
Terminal-Corpus is a large-scale Supervised Fine-Tuning (SFT) dataset designed to scale the terminal interaction capabilities of Large Language Models (LLMs). Developed by NVIDIA, this dataset was built using the Terminal-Task-Gen pipeline, which combines dataset adaptation with synthetic task generation across diverse domains.
🚀 Key Results & Performance
The high-quality trajectories in Terminal-Corpus enable… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus.nemotron-cc-translated
Helsinki-NLP/nemotron-cc-translated
nemotron-cc-tanslated is a collection of automatically translated documents from nemotron-cc taken out of the high-quality subset.
Translations are based on OPUS-MT and HPLT-MT models.
The data in v1.0 covers 156,431,999 documents with over 70 billion space-searated tokens of English data translated into 36 languages.
The total v1.0 data set includes over 2.4 trillion tokens and the translated documents are aligned across all languages.
v1.1… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/nemotron-cc-translated.Nemotron-CC-v2
Nemotron-Pre-Training-Dataset-v1 Release
Data Overview
This pretraining dataset, for generative AI model training, preserves high-value math and code while enriching it with diverse multilingual Q&A, fueling the next generation of intelligent, globally-capable models.
This dataset supports NVIDIA Nemotron Nano 2, a family of large language models (LLMs) that consists of the NVIDIA-Nemotron-Nano-9B-v2, NVIDIA-Nemotron-Nano-9B-v2-Base, and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-CC-v2.Nemotron-CC-Math-v1
Nemotron-Pre-Training-Dataset-v1 Release
👩💻 Authors: Rabeeh Karimi Mahabadi, Sanjeev Satheesh
📘 Paper: Nemotron-cc-math: A 133 Billion-Token-Scale High Quality Math Pretraining Dataset
📝 Blog: Nemotron-cc-math blog
Data Overview
We’re excited to introduce Nemotron-CC-Math - a large-scale, high-quality math corpus extracted from Common Crawl which was used in nemotron pre-training.
This dataset is built to preserve and surface high-value mathematical and code content… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-CC-Math-v1.Nemotron-CC-v2.1
Nemotron-Pre-Training-Dataset-v2.1
Dataset Description
The Nemotron-Pre-Training-Dataset-v2.1 extends the previously released Nemotron pretraining datasets with refreshed, higher-quality, and more diverse data across math, code, English Common Crawl, and large-scale synthetic corpora. Designed for the NVIDIA Nemotron 3 family of LLMs, the dataset introduces new Common Crawl code extraction, 2.5T new English web tokens… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-CC-v2.1.Nemotron-Math-v2
Nemotron-Math-v2
This repository contains the dataset accompanying the paper Nemotron-Math: Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision.
Code: NeMo-Skills
Documentation: NeMo-Skills Nemotron-Math-v2 Documentation
Dataset Description
Nemotron-Math-v2 is a large-scale mathematical reasoning dataset containing approximately 347K high-quality mathematical problems and 7M model-generated reasoning trajectories. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Math-v2.Nemotron-Post-Training-Dataset-v1
Nemotron-Post-Training-Dataset-v1 Release
This dataset is a compilation of SFT data that supports improvements of math, code, stem, general reasoning, and tool calling capabilities of the original Llama instruct model Llama-3.3-Nemotron-Super-49B-v1.5.
Llama-3.3-Nemotron-Super-49B-v1.5 is an LLM which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model).
Llama-3.3-Nemotron-Super-49B-v1.5 offers a great tradeoff between model accuracy and efficiency. Efficiency… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v1.Nemotron-VLM-Dataset-v2
Nemotron-VLM-Dataset v2
Versions
Date
Commit
Changes
2025-11-05
head
Fix nights_cot dataset. Fix/filter broken <think> entries. Update fintabnet instructions. Update indexes.
2025-10-28
214051e
Initial Release
Dataset Description
Following up on Llama Nemotron VLM Dataset V1 with 3 million samples, we are releasing the Nemotron VLM Dataset V2 with almost three times as many high-quality samples.
This time, our focus was on three… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-VLM-Dataset-v2.Nemotron-Cascade-2-SFT-Data
Nemotron-Cascade-2-SFT-Data
We release the SFT data used for training Nemotron-Cascade-2.
Data sources
Math
Our non-proof math prompts are sourced from Nemotron-Cascade-1-SFT and Nemotron-Math-v2, with responses generated by DeepSeek-V3.2, DeepSeek-V3.2-Speciale, and GPT-OSS-120B. For mathematical proofs, prompts are taken from Nemotron-Math-Proofs-v1 and generated using DeepSeek-V3.2-Speciale.
Science
We collect science prompts from… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Cascade-2-SFT-Data.Nemotron-Image-Training-v3
Nemotron Image Training v3
Versions
Date
Commit
Changes
2026-04-28
HEAD
Initial commit.
Dataset Description
Nemotron Image Training v3 is a collection of image-centric multimodal training data for vision–language models. Similar to Nemotron-VLM-Dataset v2, it was curated as a large-scale, multi-subdataset release where each subset ships a standardized conversation JSONL alongside a dataset card describing sources, licensing, and media layout.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Image-Training-v3.Nemotron-SFT-Math-v4
Nemotron-SFT-Math-v4
Dataset Description:
Nemotron-SFT-Math-v4 is a large-scale mathematical reasoning dataset containing model-generated reasoning trajectories. Solutions in this version are generated using DeepSeek-V4-Pro on High inference mode.
The problems in this dataset are sourced from nvidia/Nemotron-Math-v2, which contains high-quality mathematical problems derived from the Art of Problem Solving (AoPS) community and Math StackExchange/MathOverflow… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Math-v4.Llama-Nemotron-Post-Training-Dataset
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset.Nemotron-Personas-Korea
Nemotron-Personas-Korea
우리나라 실제 분포에 기반한 합성 페르소나를 위한 복합 AI 시스템
A compound AI approach to personas grounded in real-world distributions
데이터셋 개요 (Overview)
Nemotron-Personas-Korea는 대한민국의 실제 인구통계학적·지리적·성격 특성 분포를 기반으로 합성된 오픈소스 페르소나 데이터셋(CC BY 4.0)으로, 우리나라 인구의 다양성과 특성을 폭넓게 반영하도록 설계되었습니다. 이는 최초의 대규모 우리말 페르소나 데이터셋이며, 이름, 성별, 나이, 혼인 상태, 교육 수준, 직업, 거주 지역 등의 속성을 실제 대한민국 국가데이터처 국가통계포털(KOSIS), 대법원, 국민건강보험공단, 농촌경제연구원, NAVER Cloud 통계 자료를 기반으로 합성하였습니다.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas-Korea.Nemotron-SFT-Science-v2
Dataset Description:
Nemotron-Science-v2 is a science reasoning dataset with synthetic (synthetic MCQ, RQA) and non-synthetic vendor problems and LLM-generated solutions. It comprises three domains (Physics, Biology, and Chemistry), two question formats (multiple-choice questions [MCQ] and open questions [OpenQ]), and three generation setups: chain-of-thought (CoT) reasoning without tools, Python tool usage, and search tools usage with the Tavily API.
The solutions were… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Science-v2.nemotron-3-nano-30b-20260719-spare-games-envs
Nemotron-3-Nano-30B SPARE Self-Play Environments (run_20260719_final)
This dataset packages the self-play generated game environments produced
by a live SPARE (Self-Play with Adaptive cuRriculum Extension) training run
of NVIDIA-Nemotron-3-Nano-30B-A3B. It is a raw-data export for another
agent to pick up, replay, and build its own visualization / weave log from.
Provenance
Run: run_20260719_final
Source Ray job: spare_nemotron_games_mtpg768_1784556397 (the live… See the full description on the dataset page: https://huggingface.co/datasets/msr-spare-1/nemotron-3-nano-30b-20260719-spare-games-envs.Nemotron-Post-Training-Dataset-v2
Nemotron-Post-Training-Dataset-v2 Release
Data Overview
This dataset adds to NVIDIA’s post-training dataset releases with an extension of SFT and RL data into five target languages: Spanish, French, German, Italian and Japanese. The data supports improvements of math, code, general reasoning, and instruction following capabilities of the NVIDIA-Nemotron-Nano-9B-v2-Base, in support of release of NVIDIA-Nemotron-Nano-8B-v2-Reasoning.
NVIDIA-Nemotron-Nano-9B is a family of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2.Nemotron-Pretraining-Specialized-v1
Nemotron-Pre-Training-Dataset-v2.1
Dataset Description
The Nemotron-Pre-Training-Dataset-v2.1 extends the previously released Nemotron pretraining datasets with refreshed, higher-quality, and more diverse data across math, code, English Common Crawl, and large-scale synthetic corpora. Designed for the NVIDIA Nemotron 3 family of LLMs, the dataset introduces new Common Crawl code extraction, 2.5T new English web tokens… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.Nemotron-Pretraining-Code-v1
Nemotron-Pre-Training-Dataset-v1 Release
Data Overview
This pretraining dataset, for generative AI model training, preserves high-value math and code while enriching it with diverse multilingual Q&A, fueling the next generation of intelligent, globally-capable models.
This dataset supports NVIDIA Nemotron Nano 2, a family of large language models (LLMs) that consists of the NVIDIA-Nemotron-Nano-9B-v2, NVIDIA-Nemotron-Nano-9B-v2-Base, and NVIDIA-Nemotron-Nano-12B-v2-Base… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Code-v1.Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.Nemotron-Pretraining-Code-v2
Nemotron-Pre-Training-Dataset-v2.1
Dataset Description
The Nemotron-Pre-Training-Dataset-v2.1 extends the previously released Nemotron pretraining datasets with refreshed, higher-quality, and more diverse data across math, code, English Common Crawl, and large-scale synthetic corpora. Designed for the NVIDIA Nemotron 3 family of LLMs, the dataset introduces new Common Crawl code extraction, 2.5T new English web tokens… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Code-v2.Nemotron-PII
Nemotron-PII: Synthesized Data for Privacy-Preserving AI
Dataset Description
Nemotron‑PII is a synthetic, persona‑grounded dataset for training and evaluating detection of Personally Identifiable Information (PII) and Protected Health Information (PHI) in text at production quality. It contains 100,000 English records across 50+ industries with span‑level annotations for 55+ PII/PHI categories, generated with NVIDIA NeMo Data Designer using synthetic personas grounded in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-PII.Nemotron-Personas-Japan
Nemotron-Personas-Japan
現実世界の分布に基づいたペルソナ生成のための複合AIアプローチ
データセット概要 (Dataset Overview)
Nemotron-Personas-Japan は、日本における人口の多様性と豊かさを捉えることを目的とし、実世界の人口統計、地理的分布、性格特性の分布に基づいて合成的に生成されたペルソナのオープンソースデータセットです。名前、性別、年齢、背景、婚姻状況、学歴、職業、居住地などの統計に基づいて生成した初のデータセットされた Nemotron-Personas の日本語版です。本バージョンでは、日本語における多様なモデリングユースケースに適した高品質のペルソナを提供します
Nemotron-Personas-Japan は、日本のモデル開発者が重要な地域固有の人口統計や文化的背景を取り入れたソブリンAIシステムを開発することを支援します。本データセットは、日本の地理的・人口統計的な実分布を反映することで、合成データの多様性を高め、バイアスを軽減し、model… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas-Japan.Nemotron-Personas-USA
Nemotron-Personas-USA
A compound AI approach to personas grounded in real-world distributions
v1.1 Update
The v1.1 update introduces the following changes:
leverage openai/gpt-oss-120b model instead of mistralai/Mixtral-8x22B-v0.1 model to improve data quality and diversity
increase the number of records from 100k to 1M, for a total of 0.94B tokens
update the dataset name to Nemotron-Personas-USA in order to differentiate it from other region-specific datasets… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA.Llama-Nemotron-VLM-Dataset-v1
Llama-Nemotron-VLM-Dataset v1
Versions
Date
Commit
Changes
2025-08-11
bdb3899
Initial release
2025-08-18
5abc7df
Fixes bug (ocr_1 and ocr_3 images were swapped)
2025-08-19
ef85bef
Update instructions for ocr_9
2025-08-25
4e46f2b
Added example for Megatron Energon
2025-09-02
head
Update license headers
Quickstart
If you want to dive in right away and load some samples using Megatron Energon, check out this section below.
Data… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-VLM-Dataset-v1.Nemotron-RL-agent-workplace_assistant
Dataset Description:
The Nemotron-RL-agent-workplace_assistant is a tool use - multi step agentic environment that tests the agent’s ability to execute tasks in a workplace setting. Workbench contains a sandbox environment with five databases, 26 tools, and 690 tasks. These tasks represent common business activities, such as sending emails, scheduling meetings, etc.
This dataset is released as part of NVIDIA NeMo Gym, a framework for building reinforcement learning environments… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-agent-workplace_assistant.Nemotron-SFT-SWE-v3.5
Nemotron-SFT-SWE-v3.5
Dataset Description:
Nemotron-SFT-SWE-v3.5 is a software engineering instruction-tuning dataset designed to advance the capabilities of large language models (LLMs) on software engineering (SWE)-style tasks. The seed tasks model real-world coding applications requiring changes across multiple files and artifacts, including source code, tests, documentation, and configuration. The dataset contains agentic trajectories collected using the… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-SWE-v3.5.Nemotron-SFT-SWE-v3
Dataset Description:
Nemotron-SFT-SWE-v3 is a software engineering instruction tuning dataset designed to advance the capabilities of LLMs on SWE-Bench style tasks.
It includes agentic trajectories collected using a variety of agent harnesses, including the OpenHands, SWE-agent, and mini-SWE-agent frameworks.
This dataset is ready for commercial use.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
Created on: 2026-06-04
Last Modified… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-SWE-v3.Nemotron-Science-v1
Dataset Description:
Nemotron-Science-v1 is a synthetic science reasoning dataset with two subsets: an MCQA set that improves on the STEM portion of Nemotron-Post-Training-v1 using GPT-OSS-120B to generate GPQA-style questions and reasoning traces, and an RQA set of synthetic chemistry questions.
This dataset is ready for commercial use.
The Nemotron-Science-v1 dataset contains the following subsets:
MCQA
This subset is an improvement of the STEM subset in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Science-v1.Nemotron-Math-Proofs-v3-SFT
Nemotron-Math-Proofs-v3-SFT
Dataset Description:
Nemotron-Math-Proofs-v3-SFT is a long-form mathematical reasoning dataset containing proof-generation, proof-refinement, verification, and meta-verification traces. The release contains 414,890 samples representing 15,818 unique problems after quality filtering.
The source pool contains 15,879 hard proof problems selected from the AoPS subset of nvidia/Nemotron-Math-Proofs-v1. Responses are generated using… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v3-SFT.