datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenMathInstruct-2
OpenMathInstruct-2
OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs
generated using the Llama3.1-405B-Instruct model.
The training set problems of GSM8K
and MATH are used for constructing the dataset in the following ways:
Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH.
Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.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.OpenMathReasoning
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
306K unique mathematical problems sourced from AoPS forums with:
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
Additional 193K problems sourced from AoPS forums (problems only, no solutions)
We used… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathReasoning.OpenMathInstruct-1
OpenMathInstruct-1
OpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed Mixtral-8x7B model.
The problems are from GSM8K
and MATH training subsets and the solutions
are synthetically generated by allowing Mixtral model to use a mix of text reasoning and
code blocks executed by Python interpreter.
The dataset is split into train and validation subsets that we used in the ablations experiments.
These two subsets… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-1.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-SpecializedDomains-Finance-v1
Dataset Description
Nemotron-SpecializedDomains-Finance is a large-scale synthetic financial question-answering dataset designed to improve LLM performance on specialized financial reasoning and document comprehension tasks. The dataset comprises 326K+ high-quality Q&A pairs generated from SEC filings of S&P 500 companies spanning 2019-2024.
This dataset is ready for commercial use.
Overview
The dataset leverages template-based Synthetic Data Generation (SDG) to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SpecializedDomains-Finance-v1.Nemotron-CrossThink
Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
Author: Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, Seungju Han, Ying Lin, Evelina Bakhturina, Eric Nyberg, Yejin Choi,
Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
[Paper][Blog]
Dataset Description
Nemotron-CrossThink is a multi-domain reinforcement learning (RL) dataset designed to improve general-purpose
and mathematical reasoning in large language models (LLMs).
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-CrossThink.Nemotron-RL-ARC-AGI-v1
Dataset Description:
Nemotron-RL-ARC-AGI-v1 is a reinforcement-learning (RL) gym environment dataset of single-step ARC-AGI puzzle prompts intended for RL post-training of large language models. Each row corresponds to one ARC puzzle (a set of (input grid, output grid) demonstration pairs plus a single test input grid) rendered as a text prompt; reward is binary (1.0 / 0.0) determined by exact-match comparison against the ground-truth output grid. No LLM judge is used, no… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-ARC-AGI-v1.mirror-nvidia__OpenMathInstruct-2
OpenMathInstruct-2
OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs
generated using the Llama3.1-405B-Instruct model.
The training set problems of GSM8K
and MATH are used for constructing the dataset in the following ways:
Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH.
Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/leeaandrob/mirror-nvidia__OpenMathInstruct-2.AceMath-RewardBenchwebsite | paper
AceMath-RewardBench Evaluation Dataset Card
The AceMath-RewardBench evaluation dataset evaluates capabilities of a math reward model using the best-of-N (N=8) setting for 7 datasets:
GSM8K: 1319 questions
Math500: 500 questions
Minerva Math: 272 questions
Gaokao 2023 en: 385 questions
OlympiadBench: 675 questions
College Math: 2818 questions
MMLU STEM: 3018 questions
Each example in the dataset contains:
A mathematical question
64 solution attempts with varying… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/AceMath-RewardBench.Nemotron-RL-litmus-bench-v0.1
Dataset Description:
Litmus-Bench v0.1 is an open dataset for training and evaluating chemical reasoning in language models. It includes 5,232 training questions and 482 test questions, each in short-answer format and was created from the ChEMBL dataset with RDKit descriptors requiring short answers. The dataset is for RL training.
This dataset is released as part of NVIDIA NeMo-Gym, an open-source library within the NVIDIA NeMo framework, designed for large-scale, verifiable… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-litmus-bench-v0.1.Retrieval-Synthetic-NVDocs-v1
Dataset Description:
Retrieval-Synthetic-NVDocs-v1 is a synthetic retrieval dataset with question–answer supervision designed to train and evaluate embedding and RAG systems. The dataset was generated on top of NVIDIA's publicly available content using NeMo Data Designer, NVIDIA's open-source framework for generating high-quality synthetic data from scratch or based on seed data.
The dataset contains document chunks paired with semantically rich question-answer pairs across multiple… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Retrieval-Synthetic-NVDocs-v1.Nemotron-RL-QA-Abstention-v1Nemotron-RL-QA-Abstention-v1
License: cc-by-4.0
Language: en
Task Categories: reinforcement-learning, question-answering, text-generation
Tags: abstention, question-answering, hotpotqa, software-engineering, health, law, rl, rlvr
Configs: default train split at data/train.jsonl
Domain: multi-domain question answering, abstention
Modality: text
Capability Breakdown: Abstention-aware factoid question answering [100%]
Source: Hybrid: Automated, Manually Collected, Synthetic
Size Bin: <10K… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-QA-Abstention-v1.OpenMath-GSM8K-masked
OpenMath GSM8K Masked
We release a masked version of the GSM8K solutions.
This data can be used to aid synthetic generation of additional solutions for GSM8K dataset
as it is much less likely to lead to inconsistent reasoning compared to using
the original solutions directly.
This dataset was used to construct OpenMathInstruct-1:
a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed Mixtral-8x7B model.
For details of how the masked… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMath-GSM8K-masked.HyperThink-X-Nvidia-Opencode-Reasoning-200K
🔮 HyperThink
HyperThink is a premium, best-in-class dataset series capturing deep reasoning interactions between users and an advanced Reasoning AI system. Designed for training and evaluating next-gen language models on complex multi-step tasks, the dataset spans a wide range of prompts and guided thinking outputs.
🚀 Dataset Tiers
HyperThink is available in three expertly curated versions, allowing flexible scaling based on compute resources and training goals:… See the full description on the dataset page: https://huggingface.co/datasets/Sashvat/HyperThink-X-Nvidia-Opencode-Reasoning-200K.OpenMath-MATH-masked
OpenMath GSM8K Masked
We release a masked version of the MATH solutions.
This data can be used to aid synthetic generation of additional solutions for MATH dataset
as it is much less likely to lead to inconsistent reasoning compared to using
the original solutions directly.
This dataset was used to construct OpenMathInstruct-1:
a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed Mixtral-8x7B model.
For details of how the masked… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMath-MATH-masked.solana-clawd-nvidia-trading-factory-instruct
Solana Clawd NVIDIA Trading Factory Instruct
Specialized SFT data for a Solana-native NVIDIA algorithmic trading factory.
It teaches data ingestion, GPU feature engineering, alpha research, cuML KDE
scenario generation, cuFOLIO/cuOpt Mean-CVaR optimization, paper execution
policy, risk controls, backtesting, monitoring, and Clawd governance.
Format
Each row uses OpenAI-style messages plus metadata:
{"messages": [{"role": "system", "content": "..."}, {"role":… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-nvidia-trading-factory-instruct.nvidia-Nemotron-Science-Math
NVIDIA Nemotron Science and Math Reasoning
This is an unofficial, curated collection derived from NVIDIA's open-source Nemotron datasets. It is designed specifically to train language models in complex scientific and mathematical reasoning by providing structured chain-of-thought (CoT) examples.
To ensure efficiency, the shortest available CoT sequence was chosen for each question, filtering out redundant variations while preserving the core logical progression toward the final… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/nvidia-Nemotron-Science-Math.Vietnamese-nvidia-OpenMathInstruct-1-50k-gg-translatedfine-tune-nvidia-blackwellNvidiaDocumentationQandApairs-llama2nvidia-nemotron-model-reasoning-dataset-turkish
Nemotron Reasoning Challenge - Turkish
Turkish translation of the training data from NVIDIA's Nemotron Model Reasoning Challenge
Each row is a reasoning puzzle framed in an "Alice's Wonderland" setting. Given a few input/output examples, the model needs to figure out the hidden rule and apply it to a new input.
Category
Rows
Description
bit
1602
Hidden bit manipulation rule on 8-bit binary numbers
grav
1597
Falling distance with a modified gravitational constant… See the full description on the dataset page: https://huggingface.co/datasets/mramazan/nvidia-nemotron-model-reasoning-dataset-turkish.cleaned_nvidia_OpenCodeReasoning元データ: https://huggingface.co/datasets/nvidia/OpenCodeReasoning
データ件数: 11,275
平均トークン数: 11251
最大トークン数: 19,802
合計トークン数: 126,859,041
ファイル形式: JSONL
ファイルサイズ: 707.4 MB
難易度スコアが15, カテゴリがcompetition、ライセンスがmitとcc-by-4.0をピックアップ
繰り返し除去
極端に少ない・多いなどを除去
詳しいコードはGithub
https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/opencodereasoning
Arabic_NVIDIA
Dataset Overview
This dataset contains texts translated into Arabic, where the NVIDIA data, specifically the nvidia/HelpSteer2 dataset,
was translated using Google Translate to achieve accurate translation.
This data is specifically designed for use in question answering and text generation tasks.
Dataset Details
Original Dataset: NVIDIA's HelpSteer2 dataset.
Language: Translated into Arabic.
Use Case: question answering and text generation tasks.