datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MedQA-Mixtral-CoT
Dataset Card for medqa-cot
Synthetically enhanced responses to the medqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the question… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedQA-Mixtral-CoT.medical_cot
Medical Question-Answering Dataset
A comprehensive collection of medical questions and detailed answers, designed for training and evaluating medical question-answering systems.
Dataset Description
Overview
This dataset contains medical questions with multiple-choice answers and detailed explanations. Each question presents a clinical scenario and requires medical knowledge to determine the correct diagnosis, treatment, or underlying mechanism.
Data… See the full description on the dataset page: https://huggingface.co/datasets/blue-blues/medical_cot.headqa-cot-llama31
headqa-cot
Synthetically enhanced responses to the HeadQA dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the HeadQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a hand-crafted list of… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/headqa-cot-llama31.MMLU-medical-cot-llama31
MMLU-medical-cot
Synthetically enhanced responses to the medical-related questions of the auxiliary train set of the MMLU dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
First, we use Llama-3.1-70B-Instruct to filter the medical-related questions of the auxiliary train set of the MMLU dataset. Next, we leverage Mixtral-8x7B to… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MMLU-medical-cot-llama31.Cot-Drop
LuminaSFT
LuminaSFT is a synthetic SFT dataset suite specifically designed to improve both general-purpose and task-specific SLMs. LuminaSFT consists of multiple curated splits that target diverse capabilities:
UltraChat200K-DeepSeek - A regenerated base SFT dataset for broad instruction following.
InstructGPT-NaturalQA and InstructGPT-TriviaQA - Factual question answering datasets to strengthen knowledge recall and answer accuracy.
CoT-Drop - A reading comprehension dataset with… See the full description on the dataset page: https://huggingface.co/datasets/amd/Cot-Drop.MedMCQA-Mixtral-CoT
Dataset Card for medmcqa-cot
Synthetically enhanced responses to the medmcqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedMCQA-Mixtral-CoT.salabs-stem-deep-reasoning-cot-v13
🧪 SALabs Multi-Domain STEM Deep Reasoning & Chain-of-Thought (CoT) Corpus (v13.0)
[!IMPORTANT]
💳 Click Here to Purchase Enterprise Commercial License ($2,500 USD) & Instant 31.7MB Master Archive DownloadInstant download of the full lossless master package containing all 1,816 JSONL reasoning records + 13 complete uncompressed text corpora (31.72 MB uncompressed total) + commercial license certificate.
🌟 Executive Summary
The SALabs STEM Deep Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/suitai/salabs-stem-deep-reasoning-cot-v13.Bilge-Turkish-CoT-50K
Bilge: Turkish Chain-of-Thought Dataset (50K)
50,000 örneklik Türkçe Chain-of-Thought (CoT) reasoning fine-tuning veri seti.
Bilge, Türkçe büyük dil modellerinin adım adım düşünme (reasoning) kapasitesini
geliştirmek amacıyla hazırlanmış bir Chain-of-Thought veri setidir.
Veri setindeki her örnek, modelin önce <think> blokları içinde görünür bir
muhakeme süreci yürütmesini, ardından kullanıcıya yapılandırılmış ve detaylı
bir cevap vermesini öğretmek üzere tasarlanmıştır.
Bu… See the full description on the dataset page: https://huggingface.co/datasets/bugrabilge/Bilge-Turkish-CoT-50K.medqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedQa dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a hand-crafted… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medqa-cot-llama31.Turkish-CoT-Instruct-Dataset
🇹🇷 Turkish CoT Instruct Dataset
Türkçe Düşünme Zinciri (Chain-of-Thought) İçeren Talimat Veri Seti
Bu veri seti, modellerin Türkçe adım adım akıl yürütme (reasoning) yeteneğini
geliştirmek için hazırlanmıştır. Her örnekte model, cevabı vermeden önce
<think> ... </think> etiketleri arasında tamamen Türkçe olarak adım adım
düşünür, ardından ayrıntılı bir nihai cevap sunar (DeepSeek-R1 tarzı biçim).
Örnek sayısı: 4.868
Dil: Türkçe
Biçim: Sohbet (messages) — system / user /… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-CoT-Instruct-Dataset.glm-4.7-multiturn-CoT
glm-4.7-multiturn-CoT
Dataset Summary
glm-4.7-multiturn-CoT is a ShareGPT-style multi-turn reasoning distillation dataset generated with GLM-4.7 as the teacher model.
This release focuses on preserving multi-turn dialogue continuity while injecting explicit chain-of-thought style responses in assistant turns.
Key Features
Multi-turn conversation format (human / gpt)
Assistant responses stored as <think>...</think> + final answer
Resume-safe distillation… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/glm-4.7-multiturn-CoT.gsm8k-cot-120b
🚀 GSM8K-Teacher-CoT-120B (2025)
High-Quality Short Chain-of-Thought Distillation Dataset
Plain Text • No LaTeX • No ChatML • Deterministic Final Answers
This dataset provides high-quality short Chain-of-Thought (CoT) reasoning generated by OpenAI gpt-oss-120b on the GSM8K benchmark.
It is designed for small reasoning models (7B–14B).
The dataset is:
✔ plain text
✔ concise and deterministic
✔ fully normalized
✔ free of LaTeX, ChatML, XML, Markdown
✔ optimized for tokenizer… See the full description on the dataset page: https://huggingface.co/datasets/HAD653/gsm8k-cot-120b.medmcqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedMCQA dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medmcqa-cot-llama31.deepseek-v4-pro-math-cot-1k
DeepSeek V4 Pro Math CoT 1K
A small, high-signal supervised-fine-tuning (SFT) dataset of math reasoning traces. Problems were sampled from a Nemotron math problem set (originally sourced from StackExchange-Math and AoPS), answered by DeepSeek V4 Pro with thinking enabled at high reasoning effort, then independently reviewed by DeepSeek V4 Flash for correctness against the expected answer. Pathological reasoning traces (looping, run-away length, excessive Wait-style backtracking)… See the full description on the dataset page: https://huggingface.co/datasets/blythet/deepseek-v4-pro-math-cot-1k.math-sft-solutions-no-cot
Math SFT Solutions No CoT
A cleaned mathematics supervised fine-tuning dataset containing:
instruction → solution pairs
mathematical proofs
derivations
olympiad-style solutions
theorem reasoning
stepwise mathematical explanations
detailed final solutions
This dataset was built specifically for mathematical supervised fine-tuning (SFT).
Unlike many reasoning datasets, this release removes explicit chain-of-thought tags and hidden thinking traces while preserving high-quality… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-harsh-99/math-sft-solutions-no-cot.elementary_math_cot
Elementary Math QA with Chain-of-Thought
Dataset Description
This dataset contains synthetically generated elementary math questions
(addition, subtraction, multiplication, division, order of
operations/PEMDAS, percentages, exponents, square roots, and averages),
each paired with a step-by-step chain-of-thought (CoT) explanation and a
final answer.
Problems and their ground-truth answers are generated deterministically
in Python, so every final answer is exact… See the full description on the dataset page: https://huggingface.co/datasets/alexfromapex/elementary_math_cot.PubmedQA-Mixtral-CoT
Dataset Card for pubmedqa-cot
Synthetically enhanced responses to the pubmedqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the PubMedQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/PubmedQA-Mixtral-CoT.math-sft-solutions-no-cot-v3
Math SFT Solutions No CoT V3
Math SFT Solutions No CoT V3 is a large-scale mathematics supervised fine-tuning (SFT) dataset designed for instruction tuning and mathematical capability adaptation.
Version 3 substantially expands mathematical coverage while improving dataset quality through stronger filtering, cleaning, and supervision refinement.
Unlike reasoning-heavy datasets, this release focuses on clean instruction → response pairs without hidden chain-of-thought style… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-harsh-99/math-sft-solutions-no-cot-v3.PRISM-CoT
PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality
This repository contains the PRISM-CoT and PRISM-DPO datasets, which are key components of the PRISM (Principled Reasoning for Integrated Safety in Multimodality) framework. PRISM is a system2-like framework designed to align Vision-Language Models (VLMs) by embedding a structured, safety-aware reasoning process.
PRISM-CoT is a dataset that teaches safety-aware chain-of-thought reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/andyc03/PRISM-CoT.Rehber-CoT-Science
🧬 Rehber-CoT-Science: Turkish Scientific Reasoning Dataset
Turkish Scientific Computational Reasoning (Chain-of-Thought) Dataset
Multi-step scientific problem-solving dataset with verifiable Python code and detailed explanations
Dataset • Author
📌 Changelog
Eski sürümlere erişim: Branch menüsünden v1 seçebilirsiniz.
Version
Date
Changes
v2.0
24.12.2025
✨ Yeni explained_answer alanı eklendi, Statistics domain eklendi, 712 örneğe genişletildi… See the full description on the dataset page: https://huggingface.co/datasets/batuhanozkose/Rehber-CoT-Science.AfriHist-CoT
AfriHist-CoT
Overview
AfriHist-CoT is a dataset of question-answer pairs derived from African history books, created using a Chain-of-Thought (CoT) reasoning approach with the Gemini language model via OpenRouter. The dataset supports training and evaluating question-answering models, with a focus on African history and CoT reasoning. It is available in English and French, catering to both monolingual and multilingual applications.
Dataset Description
The… See the full description on the dataset page: https://huggingface.co/datasets/Svngoku/AfriHist-CoT.simplemath-cot
🧮 SimpleMath-100k CoT
A chain-of-thought (CoT) extension of the
ProCreations/SimpleMath
dataset. Every one of the 100 000 algebra / arithmetic problems is paired with a
short, numbered reasoning trace (Step 1: … Step 2: …) that walks a language
model from the problem statement to the known-correct answer.
The traces in the Jupyter notebook are generated by
Qwen3.8-27B and then post-processed to strip formatting noise,
enforce sequential step numbering, and cap output at 1 000… See the full description on the dataset page: https://huggingface.co/datasets/alexfromapex/simplemath-cot.cot-dataDataset used in the paper Dyve: Thinking Fast and Slow for Dynamic Process Verification.
Github:
https://github.com/Jianyuan1/Dyve
pubmedqa_cot_folko-verified-cot
ko-verified-cot
2,016 short Korean reasoning traces whose final answer was verified against a ground-truth
answer key. Wrong reasoning was thrown away, not kept.
How it was built
Sample multiple-choice questions from the train split of
KMMLU (45 subjects) — test split is never
touched, so downstream evaluation stays clean.
Ask the teacher (ox-alpha-free) for a short Korean chain of thought (3–4 sentences) ending
in 정답: X. The teacher never sees the answer key.… See the full description on the dataset page: https://huggingface.co/datasets/waylake/ko-verified-cot.concept-cot-conv-qa-full
Concept CoT Conversational QA (Full)
Conversational QA pairs about chain-of-thought reasoning traces, generated using DeepSeek v3.2 via OpenRouter. Designed for training activation oracles to answer natural language questions about what a model is doing during reasoning.
Overview
Total pairs: 10,499
Unique source entries: 7,496 (from 8,132 concept corpus entries)
Source corpus: ceselder/concept-cot-corpus-full (concept_corpus/corpus_full.jsonl)
Generator model:… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/concept-cot-conv-qa-full.Gemini-MMLU-CoT
Gemini-MMLU-CoT: An Advanced Mathematical Reasoning Dataset
A synthetic dataset of 7,000 multiple-choice mathematics questions featuring detailed Chain-of-Thought (CoT) reasoning. The content was generated by Google's Gemini model, with questions inspired by the mathematical sections of the MMLU (Massive Multitask Language Understanding) benchmark.
Overview
This dataset is designed for training and evaluating AI models on complex mathematical reasoning. It covers a wide… See the full description on the dataset page: https://huggingface.co/datasets/HenryShan/Gemini-MMLU-CoT.lemonseed-cot-math-clean
lemonseed-cot-math-clean
LemonSeed — cleaned chain-of-thought math word problems.
Contents
cot_math_clean.jsonl (3530 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Synthetic, generated programmatically for the LemonSeed 1.5B project (by Geramy L. Loveless). Data authored by Michael Anthony Falabella.
sharegpt_cot_dataset
A data set inspired by the "Reflection" method, three-dimensional thinking and cot
This is the ShareGPT format.
The data set was generated using multiple llm synthesis.
NuminaMath-CoT-filteredDataset that contains problems that appears in both QwQ-LongCoT-130K-cleaned and NuminaMath-CoT. There are approximately 100k problems where the solution is in plain-CoT manner.