datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
UltraData-SFT-2605
UltraData-SFT-2605
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-2605 is the full set of core-domain SFT data used in the post-training of MiniCPM5-1B-SFT within the MiniCPM5-1B series, and a key representative of L3 refined data in the UltraData L0-L4 tiered data management framework. It covers math, code, knowledge, instruction following, and other core domains, containing over 15 million Deep… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-SFT-2605.medical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.UltraData-SFT-Agent-2609
UltraData-SFT-Agent-2609
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-SFT-Agent-2609 is the L3 refined data for Agent instruction-tuning within UltraData's L0-L4 tiered data management framework. Built for the post-training of MiniCPM5-2B, it complements UltraData-SFT-2605 (core-domain SFT) with executable Agent trajectories. The release contains approximately 500,000 samples spanning tool use… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-SFT-Agent-2609.tulu-3-sft-personas-instruction-following
Dataset Descriptions
This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints are borrowed from the taxonomy in IFEval dataset.
To generate diverse instructions, we expand the methodology in Ge et al., 2024 by using personas. More details and exact prompts used to construct the dataset can be found in our paper.
Curated by: Allen Institute for AI
Paper: TBD… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following.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.Step-3.5-Flash-SFT
Step-3.5-Flash-SFT
Step-3.5-Flash-SFT is a general-domain supervised fine-tuning release for chat models.
This repository keeps the full training interface in one place:
json/: canonical raw training data
tokenizers/: tokenizer snapshots for Step-3.5-Flash and Qwen3, released to preserve chat-template alignment
compiled/: tokenizer-specific compiled shards for StepTronOSS training
Data Format
Each raw shard is a JSON file whose top level is a list of examples.… See the full description on the dataset page: https://huggingface.co/datasets/stepfun-ai/Step-3.5-Flash-SFT.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-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.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.SFT-Collection-v2
SFT-Collection-v2: A Unified Reasoning Corpus for Supervised Fine-Tuning
SFT-Collection-v2 is a large-scale, curated corpus for supervised fine-tuning (SFT)
of reasoning-oriented language models. It combines, filters, deduplicates, and
language-extends a broad set of public reasoning datasets into a single,
consistent schema. The collection focuses on chain-of-thought reasoning
traces across math, code, science, and general reasoning, covering English and
five additional languages… See the full description on the dataset page: https://huggingface.co/datasets/ahmad21omar/SFT-Collection-v2.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-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.creative-writing-sft-50k
Creative Writing SFT (50K)
50,000 ShareGPT-format creative writing conversations across 12 literary forms and 25 themes. Written to demonstrate craft — not just competent completion, but genuine literary quality: specific detail, earned emotion, controlled voice, purposeful structure.
Motivation
Most LLM creative writing training data optimizes for fluency and completion rather than craft. Models learn to produce writing that reads smoothly but relies on clichés… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/creative-writing-sft-50k.novel-agent-sft-dataset
All Novel Can Be Galgame — 完整数据集
中文小说叙事理解项目的完整数据集。包含 669 本中文小说的原始文本、标注和训练数据，用于训练叙事 Agent 系统。
项目地址：https://github.com/lin1753/novel2galgame
训练代码仓库：https://github.com/lin1753/novel-agent
数据规模
目录
文件数
大小
说明
training/
52
689 MB
训练用 SFT 数据 (JSONL)
raw-books/
671
327 MB
669 本原始小说
processed/
39,842
1.2 GB
按章节预处理文本
annotations/
1,626
1 MB
原始标注文件
合计
42,191
2.2 GB
目录结构
datasets/
├── training/
│ ├── base-sft/… See the full description on the dataset page: https://huggingface.co/datasets/mikuhhn1239/novel-agent-sft-dataset.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🧬 Omni-Frontier Collection
Cybersecurity · Coding · Math · Science · RSI Reasoning — one unified SFT package
A unified, deduplicated, fully-browsable distillation & SFT corpus — every row real, every row visible.
📖 Jump to
What's inside · 🔁 Aggregation audit · 🛡 Cybersecurity · 💻 Coding · 🏭 Distillation deep-dive · 🔁 RSI · 🧮 Math/Science/More · 🎓 Training guide · 🔎 Browsing · 🧹 Quality · 🗺 Roadmap · 📄 License… See the full description on the dataset page: https://huggingface.co/datasets/SHSLab/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.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.SFT-Reasoning
SFT-Reasoning
Dataset Description
Instruction-following and reasoning data prepared for supervised fine-tuning. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and question-answering text… See the full description on the dataset page: https://huggingface.co/datasets/IFM/SFT-Reasoning.AceReason-1.1-SFT
AceReason-1.1-SFT
AceReason-1.1-SFT is a diverse and high-quality supervised fine-tuning (SFT) dataset focused on math and code reasoning. It serves as the SFT training data for AceReason-Nemotron-1.1-7B, with all responses in the dataset generated by DeepSeek-R1.
AceReason-1.1-SFT contains 2,668,741 math samples and 1,301,591 code samples, covering the data sources from OpenMathReasoning, NuminaMath-CoT, OpenCodeReasoning, MagicoderEvolInstruct, opc-sft-stage2, leetcode… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/AceReason-1.1-SFT.UltraData-SFT-2605-no-think-8k-32k
UltraData-SFT-2605 · no_think · 8k–32k
A length-filtered subset of the no_think split of
openbmb/UltraData-SFT-2605,
containing conversations whose token length falls in the 8k–32k range.
This is the medium-length tier intended for standard long-context SFT.
Two companion tiers were produced from the same source:
Dataset
Length range
Records
this repo — fxmeng/UltraData-SFT-2605-no-think-8k-32k
8k–32k tokens
623,421
fxmeng/UltraData-SFT-2605-no-think-32k-200k… See the full description on the dataset page: https://huggingface.co/datasets/fxmeng/UltraData-SFT-2605-no-think-8k-32k.Nemotron-SFT-SWE-v2
Dataset Description:
Nemotron-SWE-v2 is a software engineering instruction tuning dataset designed to advance the capabilities of LLMs on SWE-Bench style tasks. It includes both agentic trajectories collected using the OpenHands framework and an agentless SWE subset for targeted sub-tasks such as code localization, code repair, and test generation.
This dataset is ready for commercial use.
Dataset Subsets:
Nemotron-SWE-v2 contains the following subsets:
Agentic… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-SWE-v2.Nemotron-SFT-Multilingual-v1
Dataset Description:
Nemotron-Multilingual-v1 is a multilingual reasoning dataset made by translating a subsample of SFT data from Nemotron-Math-v2, Nemotron-Competitive-Programming-v1, and Nemotron-Science-v1 into to 6 languages (German, French, Japanese, German, Italian, Japanese, Chinese).The original datasets were translated with Qwen2.5-14B-Instruct, then filtered with heuristics to remove translation failures and hallucinations. The STEM subsets are further post-edited with an… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Multilingual-v1.emova-sft-4m
EMOVA-SFT-4M
🤗 EMOVA-Models | 🤗 EMOVA-Datasets | 🤗 EMOVA-Demo
📄 Paper | 🌐 Project-Page | 💻 Github | 💻 EMOVA-Speech-Tokenizer-Github
Overview
EMOVA-SFT-4M is a comprehensive dataset curated for omni-modal instruction tuning, including textual, visual, and audio interactions. This dataset is created by gathering open-sourced multi-modal instruction datasets and synthesizing high-quality omni-modal conversation data to enhance user experience. This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Emova-ollm/emova-sft-4m.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.tulu-v2-sft-mixture
Dataset Card for Tulu V2 Mix
Note the ODC-BY license, indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact.
Tulu is a series of language models that are trained to act as helpful assistants.
The dataset consists of a mix of :
FLAN (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000 examples… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture.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.Nemotron-SFT-Multilingual-v2
Dataset Description:
Nemotron-SFT-Multilingual-v2 is a multilingual supervised fine-tuning (SFT) dataset for post-training text-generation models. It is generated by translating seed data from Nemotron-Math-v2, Nemotron-Competitive-Programming-v1, and Nemotron-Science-v1, adding multilingual coverage for Hindi (hi), Korean (ko), Brazilian Portuguese (pt-br), and refreshed Japanese (ja) data.
The dataset is generated with a new data processing pipeline that avoids line-breaking… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Multilingual-v2.KoHRM-Text-1.4B-sft-lora-data
KoHRM-Text-1.4B SFT and LoRA Prepared Data
This dataset repo stores curated KoHRM SFT/LoRA subsets in the same tokenized
HRM-Text V1Dataset format used by training. It is intended for quick behavior
alignment experiments after KoHRM pretraining.
Model repo:
https://huggingface.co/LLM-OS-Models/KoHRM-Text-1.4B
Code repo:
https://github.com/LLM-OS-Models/KoHRM-text
Format
Each folder is a prepared V1Dataset:
<dataset-name>/
metadata.json
tokenizer_info.json… See the full description on the dataset page: https://huggingface.co/datasets/LLM-OS-Models/KoHRM-Text-1.4B-sft-lora-data.Superior-Reasoning-SFT-gpt-oss-120b-Logprob
Superior-Reasoning-SFT-gpt-oss-120b-Logprob
🚀 Overview
This dataset contains the token-level log-probabilities generated by the teacher model (gpt-oss-120b) for the reasoning samples in the main Superior-Reasoning-SFT-gpt-oss-120b Dataset.
🔗 Relationship to Main Dataset
This dataset is a companion to the main Superior-Reasoning-SFT-gpt-oss-120bdataset. Records are linked via a unique sample_uuid.
Main Dataset: Contains the text (prompts… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b-Logprob.Nemotron-Pretraining-SFT-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-SFT-v1.zenyx-v2-SFT-dataset
Zenyx V2 — Raw SFT Dataset Collection
This is the unified raw dataset collection used for training Zenyx V2,
a custom large language model built from scratch with a novel architecture.
Dataset Sources
Dataset
Rows
Category
nemotron_sft_code
10,108,883
Code
nemotron_sft_math
22,066,397
Math
nemotron_sft_science
708,920
Science
nemotron_sft_chat
39,792
Chat
nemotron_sft_safety
31,426
Safety
nemotron_rl
56,339
Instruction Following (RL)… See the full description on the dataset page: https://huggingface.co/datasets/Arko007/zenyx-v2-SFT-dataset.