datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
🌌 Omni-Frontier Distillation SFT
The Definitive Evolution of Open-Source Distillation & Human-Crafted Expertise
Repository: Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection
"The most comprehensive multi‑domain SFT corpus ever assembled — fusing 6.86 million cleaned distillation samples with 9.14 million human‑crafted expert examples across medical, cybersecurity, chemical, robotics, humanities, and more. 16 million… See the full description on the dataset page: https://huggingface.co/datasets/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.Medical-R1-Distill-Data
Introduction
This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on medical verifiable problems from HuatuoGPT-o1.
The Chinese version of the dataset is available at FreedomIntelligence/Medical-R1-Distill-Data-Chinese.
The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data.qwen3.8-max-distillation-50k
Qwen3.8-Max Distillation 50K
A curated dataset of 49,772 teacher-generated traces from qwen3.8-max-preview, prepared for supervised fine-tuning and off-policy knowledge distillation.
The teacher responses are preserved as returned by the API. Where the model emitted visible <think>...</think> blocks, those blocks remain in the assistant message. Some simpler prompts received direct answers without a thinking block.
[!CAUTION]
Terms and provenance notice — not cleared for… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-distillation-50k.Chinese-DeepSeek-R1-Distill-data-110k
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。
该中文数据集中的数据分布如下：… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2
🧬 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/Manusagents/Omni-Frontier-Distillation-SFT-Cyber-security-Coding-dataset-collection-v2.distill-gpt4-eng-chat
Description
Introducing dataset consisting of gpt4 answers to users requests. Queries were taken from allenai/WildChat-1M and causal-lm/instructions. Texts (requests and responses) were deleted in 3 cases:
either has non-english letters and special symbols
either has http-links
either has html blocks
either has perplexity more than 1.5*IQR + third quantile ( in some cases average perplexity value of sentences or maximum value was used )
Chinese-DeepSeek-R1-Distill-data-110k-SFT
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：该版本为，可以直接SFT使用的版本，将原始数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT.Claude-Distills
Claude-Distills
A curated collection of open-source Claude distillation datasets, unified and deduplicated.
Note: This repo only provides unified formatting, deduplication, and documentation. All credits go to the original data creators. I did NOT create any of the original data.
Data Sources
Source
Samples
Description
claude-sonnet-4.6-120000x
119,446
Claude Sonnet 4.6 general, code, math, psychology data
claude-opus-4.6-10000x
9,633
Claude Opus… See the full description on the dataset page: https://huggingface.co/datasets/clzoro/Claude-Distills.math_distill
MATH Distill
This repository contains distillation data for MATH-500 and the full MATH dataset (minus the MATH-500 questions) generated by DeepSeek R1 and Qwen3-235B-A22B. (For more information about the MATH input dataset, see https://huggingface.co/datasets/rasbt/math_full_minus_math500.)
Construction
Source 1: 500 problems from the MATH-500 dataset (HuggingFaceH4/MATH-500)
Source 2: 12,000 problems from the MATH dataset by Hendrycks et al.… See the full description on the dataset page: https://huggingface.co/datasets/rasbt/math_distill.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/TypeSafeAI/Omni-Frontier-Distillation-SFT-Cyber-Coding-Med-dataset-collection.DeepSeek-R1-Distill-Qwen-1.5B-Self-CalibrationThis dataset contains data for the paper Efficient Test-Time Scaling via Self-Calibration.
We propose an efficient test-time scaling method by using model confidence for dynamically sampling adjustment, since confidence can be seen as an intrinsic measure that directly reflects model uncertainty on different tasks. For example, we can incorporate the model’s confidence into self-consistency by assigning each sampled response $y_i$ a confidence score $c_i$. Instead of treating all responses… See the full description on the dataset page: https://huggingface.co/datasets/HINT-lab/DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration.Medical-R1-Distill-Data-Chinese
Introduction
This dataset is an SFT dataset distilled from Deepseek-R1 (Full Power Version), based on Chinese medical verifiable problems from HuatuoGPT-o1.
The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long reasoning chains based on GPT-4o on medical-o1-reasoning-SFT.
For details, see our paper and GitHub… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data-Chinese.HealthCareMagic-DistilledThis dataset undergone:
Comprehensive data augmentation pipeline,
Soft/hard label distillation from MedGemma-27B-Text-IT
Reason-Distill
Reason-Distill
Dataset description
This Reasoning Distillation dataset is a mix of OpenThoughts and OpenR1-Math datasets, filtered for reasoning distillation. It can be used for supervised fine-tuning for reasoning distillation.
You can load the dataset as follows:
from datasets import load_dataset
dataset = load_dataset("SmallDoge/Reason-Distill")
License
The dataset is licensed under Apache 2.0.
Chinese-Qwen3-235B-Thinking-2507-Distill-100k
📌 Note: The English translation of this dataset card is provided below.
Chinese-Qwen3-235B-Thinking-2507-Distill-100k
Dataset Summary
Chinese-Qwen3-235B-Thinking-2507-Distill-100k 是一个包含约 100k 条高质量中文推理与指令数据的数据集，由 Qwen-3-235B-A22B-Thinking-2507（官方 Thinking 模式，上下文长度 32K）蒸馏生成。
该数据集覆盖了多个重要领域：
数学与工程任务（Mathematics, Applied Math, Advanced Math）
通用知识与写作（General Knowledge, Language & Writing）
技术与编程（Technology & Programming）
商业与经济（Business & Economics）… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Chinese-Qwen3-235B-Thinking-2507-Distill-100k.bielik-distill-polish-10k
bielik-distill-polish-10k
Polish instruction-tuning dataset with 10,304 samples generated via response-level knowledge distillation from Bielik-11B-v3.0-Instruct (SpeakLeash, Apache 2.0).
Covers Polish history, culture, politics, science, geography, idioms, and general reasoning. Multi-pass quality control: factual corrections, topic filtering (Poland/Europe focus), truncation removal (~9% of raw data removed).
Format
{
"messages": [
{"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/JohnTdi/bielik-distill-polish-10k.nemotron-nano2-safety-distill-gptoss
Nemotron Nano 2 Safety Distill — GPT-OSS
A distilled safety dataset produced using the Nemotron Nano 2 recipe with GPT-OSS-20B and GPT-OSS-120B as teacher models.
⚠️ Content Warning: This dataset includes potentially harmful prompts. Use responsibly for research purposes only.
Overview
This safety-focused distilled dataset was created by following the Nemotron Nano 2 safety recipe, adapted to use GPT-OSS-20B and GPT-OSS-120B as teacher models. Due to resource limitations… See the full description on the dataset page: https://huggingface.co/datasets/Ericwang/nemotron-nano2-safety-distill-gptoss.gpt-oss-120B-distilled-reasoning
GPT-oss-120B-Distilled-Reasoning-math Dataset
Data Source Model: gpt-oss-120bTask Type: Mathematical Problem SolvingData Format: JSON Lines
Fields: Generator, Category, Input, Output
Core Statistics
Generated complete reasoning processes and answers using gpt-oss-120b (MXFP4).The text length of the dataset reflects the depth and complexity of its content. I have statistically analyzed the lengths of the input (question), Reasoning, and Answer.To understand the data… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/gpt-oss-120B-distilled-reasoning.GPT-OSS-120B-Distilled-Reasoning-math
GPT-oss-120B-Distilled-Reasoning-math Dataset
Data Source Model: gpt-oss-120bTask Type: Mathematical Problem SolvingData Format: JSON Lines
Fields: Generator, Category, Input, CoT_Native_Reasoning, Reasoning, Answer
Core Statistics
Generated complete reasoning processes and answers using gpt-oss-120b (MXFP4).The text length of the dataset reflects the depth and complexity of its content. I have statistically analyzed the lengths of the input (question), Reasoning, and… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GPT-OSS-120B-Distilled-Reasoning-math.hwtcm-deepseek-r1-distill-data
简介
DeepSeek蒸馏的传统中医数据集，原始数据来源于网络，未进行人工审查。
7B模型微调效果
模型表现出了推理能力，准确性有待继续验证。
我们的其他产品
中医NER：能识别方剂、本草、来源、病名、症状、证型，也许是基于BERT开源模型中识别最好的模型。中医考试题：也许是全网最早开源、数据最多的中医考试题，我们内部将其用于模型训练的性能评测数据集。中医SFT数据集：中医QA数据集，用于SFT微调。仓公：基于Qwen的指令微调模型（暂未开源）。仓公R1：基于DeepSeek蒸馏的超过100万条QA的指令微调模型，拥有强大的推理能力（暂未开源）。
。。。还有很多
Citation
If you find this project useful in your research, please consider cite:
@misc{hwtcm2024,
title={{hwtcm-deepseek-r1-distill-data} A traditional… See the full description on the dataset page: https://huggingface.co/datasets/Monor/hwtcm-deepseek-r1-distill-data.DeepSeek-R1-Distill-Qwen-32B-LeaPPaper: Learning from Peers in Reasoning Models
Project Page: https://learning-from-peers.github.io/
Code: https://github.com/tongxuluo/LeaP
MuSeR_GPT_OSS_120B_DistillationThis dataset contains ~100k synthetic medical queries and corresponding responses distilled from GPT-OSS-120B.
The generation of synthetic medical queries follows an attribute-conditioned generation method proposed in paper Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning.
We found that supervised fine-tuning on this dataset can substantially improve LLMs' medical conversational capabilities. See our paper and project page for more details.
If… See the full description on the dataset page: https://huggingface.co/datasets/zyx1234/MuSeR_GPT_OSS_120B_Distillation.GPT-OSS-20B-Distilled-Reasoning-Mini
Dataset Card for Dataset Name
GPT-OSS-20B Distilled Reasoning Dataset Mini
(Multi-stage Evaluative Refinement Method for Reasoning Generation)
Dataset Details and Description
This is a high-quality instruction fine-tuning dataset constructed through knowledge distillation, featuring detailed Chain-of-Thought (CoT) reasoning processes. The dataset is designed to enhance the capabilities of smaller language models in complex reasoning, logical analysis, and instruction… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GPT-OSS-20B-Distilled-Reasoning-Mini.qwen3.8-max-distillation-50k
Qwen3.8-Max Distillation 50K
A curated dataset of 49,772 teacher-generated traces from qwen3.8-max-preview, prepared for supervised fine-tuning and off-policy knowledge distillation.
The teacher responses are preserved as returned by the API. Where the model emitted visible <think>...</think> blocks, those blocks remain in the assistant message. Some simpler prompts received direct answers without a thinking block.
[!CAUTION]
Terms and provenance notice — not cleared for… See the full description on the dataset page: https://huggingface.co/datasets/sender44/qwen3.8-max-distillation-50k.Qwen3-235B-A22B-Instruct-2507-Distilled-chat
Qwen3-235B-A22B-Instruct-2507-Distilled-chat📚
Curated/Funded/Shared by: [Jack Rong]
Language(s): English (major), Chinese, Русский, 한국어, 日本語, others
License: [apache-2.0]
Distilled Model: 🏆Qwen/Qwen3-235B-A22B-Instruct-2507
Qwen3-235B-A22B-Instruct-2507 Benchmarks📊
Introduction:
The objectives of this project are:
Focus on chat capabilities (excluding CoT), covering cross-lingual real-world Q&A/explanation/generation;
Utilize… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Qwen3-235B-A22B-Instruct-2507-Distilled-chat.Kimi-K3-And-DeepSeek-V4-Pro-0813-Distillation-in-PT-BR
🇧 Destilação PT-BR com Raciocínio (Chain-of-Thought)
Este dataset contém exemplos de alta qualidade gerados através da destilação de modelos de ponta (Teacher Models) disponíveis via NVIDIA NIM, focados em instrução, raciocínio lógico e naturalidade em Português Brasileiro (PT-BR).
O grande diferencial deste dataset é a inclusão explícita do processo de pensamento (Chain-of-Thought / thinking) dos modelos professores, permitindo treinar modelos menores (Student Models) não… See the full description on the dataset page: https://huggingface.co/datasets/Davizig10jojo/Kimi-K3-And-DeepSeek-V4-Pro-0813-Distillation-in-PT-BR.Thai-R1-Distill-SFT
Thai R1 Distill SFT
Thai Reasoning Dataset for Supervised Finetuning
Translated by iApp Technology
Eureka-Distill
Eureka-Distill
[📂 GitHub] [📜 Paper]
This multimodal reasoning dataset extends the MM-Eureka dataset mainly by including reasoning answers, which are distilled from our finetuned Qwen2.5-VL-7B model. We provide two formats: llama_factory and verl for corresponding training frameworks.
License
Eureka-Distill is released under the Apache License 2.0. It is derived from MMK12/MM-Eureka, which is also released under Apache-2.0. Academic research use, including… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/Eureka-Distill.TQA-Distill-R1
TQA-Distill-R1
TQA-Distill-R1 is a distilled dataset designed for training and evaluating large language models (LLMs) on Table Question Answering (TQA) tasks. It was created by prompting a local deployment of DeepSeek R1 using question-table pairs sourced from two public datasets: TQA-HiTab and TQA-WTQ.The dataset focuses on multi-step reasoning, aggregation, and table understanding abilities.
📑 Dataset Summary
Each sample contains:
A table (structured in JSON… See the full description on the dataset page: https://huggingface.co/datasets/jared-zhou/TQA-Distill-R1.