datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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-student-fail-v41-clean-thinking
DeepSeek-V4.1 clean and action-only trajectories with Nemotron outcomes
DeepSeek-V4.1 reward-1 trajectories rebuilt from the complete teacher audit
under v57-test-path-component-boundary+v57-target-source-recheck. The V4.1 reward and trajectory tier do not by themselves prove
that Nemotron failed. Student outcomes are joined from
nemotron-prolike-coverage-audit-20261001.json. A student failure requires either complete
required-test results with reward 0, or an individually… See the full description on the dataset page: https://huggingface.co/datasets/zhiyuanhucs/nemotron-student-fail-v41-clean-thinking.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-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-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-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-ClimbLab
ClimbLab Dataset
🚀 Creating the highest-quality pre-training datasets for LLMs 🌟
📄 PAPER
🤗 CLIMBLAB
🤗 CLIMBMIX
🏠 HOMEPAGE
Figure 1: Continuously training a 1B model yields a 2.0% improvement over Llama-3.2-1B, demonstrating a more efficient scaling trend compared to prior models.
Figure 2: Pre-training a 1B model from scratch on ClimbMix shows better scaling effects than training on other datasets.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-ClimbLab.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-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-SFT-Agentic-v2
Dataset Description
The Nemotron-SFT-Agentic-v2 dataset is a collection of synthetic single-turn and multi-turn tool-use trajectories designed to strengthen models’ capabilities as interactive, tool-using agents. It targets tasks where the model must decompose user goals, decide when to call tools, and reason over tool outputs to complete tasks reliably and safely.
This dataset is ready for commercial use.
The dataset consolidates three internally curated components (described… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2.Nemotron-SFT-Math-v3
Dataset Description
The dataset was updated on April 27th, 2026 to fix data formatting issues!
Nemotron-Math-v3 is a large-scale mathematical reasoning dataset containing model-generated reasoning trajectories produced both with and without Python Tool-Integrated Reasoning (TIR). Chain-of-thought (CoT) solutions are generated using DeepSeek-V3.2-Speciale, while Python Tool-Integrated Reasoning (TIR) solutions are generated using DeepSeek-V3.2.
The problems in this dataset are… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Math-v3.Nemotron-Terminal-Synthetic-Tasks
Terminal-Corpus: Task Structure Specification
This repository contains the skill-based synthetic tasks within the Terminal-Corpus. These tasks are designed to evaluate and train autonomous agents in realistic Linux terminal environments.
🏗️ Task Anatomy
Each task is contained within a dedicated directory and follows a strict four-component architecture:
1. Instruction (instruction.md)
Purpose: Provides the natural language description of the objective.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Synthetic-Tasks.Nemotron-SFT-Instruction-Following-Chat-v2
Dataset Description:
The Nemotron-Instruction-Following-Chat-v2 dataset is designed to broadly strengthen the model’s interactive capabilities, including open-ended chat and precise instruction following.The dataset is a refreshed version of Nemotron-Instruction-Following-Chat-v1 with synthetic dialogues generated from Kimi-K2-Thinking, GLM-4.6, Qwen3-235B-A22B-Thinking-2507, GPT-OSS-120b, Kimi-K2-Instruct-0905, and Qwen3-235B-A22B-Instruct-2507.
This dataset is ready for commercial… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v2.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-ClimbMix
ClimbMix Dataset
🚀 Creating the highest-quality pre-training datasets for LLMs 🌟
📄 PAPER
🤗 CLIMBLAB
🤗 CLIMBMIX
🏠 HOMEPAGE
Figure 1: Continuously training a 1B model yields a 2.0% improvement over Llama-3.2-1B, demonstrating a more efficient scaling trend compared to prior models.
Figure 2: Pre-training a 1B model from scratch on ClimbMix shows better scaling effects than training on other datasets.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix.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-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-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.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-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-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-Competitive-Programming-v1
Dataset Description:
Nemotron-Competitive-Programming-v1 is a large-scale synthetic coding and reasoning dataset designed to push LLM performance on challenging programming and systems tasks. It combines Python and C++ samples across unique competitive programming questions.
Beyond problem solving, the dataset includes InfiniByte, a cross-domain subset with problems derived from scientific fields.
This dataset is ready for commercial use.
Competitive Coding
The… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Competitive-Programming-v1.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-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-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-SFT-Competitive-Programming-v2
Dataset Description:
Nemotron-Competitive-Programming-v2 is a large-scale synthetic coding and reasoning dataset designed to push LLM performance on challenging programming and systems tasks. It combines Python and C++ samples across unique competitive programming questions.In addition, the dataset includes samples targetting a subset of programming language exercises from Exercism.
Beyond problem solving, the dataset includes Text-to-SQL, which aims at training LLMs to reason and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Competitive-Programming-v2.CLM-v0.1-Pretrain-NemotronNemotron-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.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.