datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
China-K12-STEM-10K-CoT-Reasoning
K12-STEM-CoT-Chinese
1.54M Chinese K12 STEM problems with chain-of-thought solutions, 48% with diagrams.
The largest structured Chinese math/physics/chemistry reasoning dataset.
This is a curated sample (10,000 problems) of the full 1.54M dataset available via API.
Full Dataset Access
Access the full 1,540,000+ problems via API →
This Sample
Full API
Total problems
10,025
1,540,000+
With CoT solutions
10,025
1,490,000+
With diagrams
6,093
740,000+… See the full description on the dataset page: https://huggingface.co/datasets/lfaviate/China-K12-STEM-10K-CoT-Reasoning.chess-reasoning-cot-evalsMagpie-Reasoning-V2-250K-CoT-Llama3
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Llama3.cot-logic-reasoningMath_CoT_Arabic_English_Reasoning
Math CoT Arabic English Dataset
A high-quality, bilingual (English & Arabic) dataset for Chain-of-Thought (COT) reasoning in mathematics and related disciplines, developed by Miscovery AI.
Overview
Math-COT is a unique dataset designed to facilitate and benchmark the development of chain-of-thought reasoning capabilities in language models across mathematical domains. With meticulously crafted examples, explicit reasoning steps, and bilingual support, this dataset offers… See the full description on the dataset page: https://huggingface.co/datasets/miscovery/Math_CoT_Arabic_English_Reasoning.cybersecurity-reasoning-cot-v1
🛡️ Expert Cybersecurity Reasoning Dataset (CoT)
This dataset contains 89 high-fidelity, expert-verified reasoning records focusing on complex cybersecurity attack vectors. It is designed specifically for fine-tuning Large Language Models (LLMs) on sophisticated security analysis and threat logic.
💎 Key Highlights
Niche Rarity 1.0: Covers rare and emerging threats with zero prior representation in open-source datasets.
Advanced Vectors: Includes detailed reasoning for… See the full description on the dataset page: https://huggingface.co/datasets/expertdata-factory/cybersecurity-reasoning-cot-v1.China-K12-STEM-10K-CoT-Reasoning
K12-STEM-CoT-Chinese
1.54M Chinese K12 STEM problems with chain-of-thought solutions, 48% with diagrams.
The largest structured Chinese math/physics/chemistry reasoning dataset.
This is a curated sample (10,000 problems) of the full 1.54M dataset available via API.
Full Dataset Access
Access the full 1,540,000+ problems via API →
This Sample
Full API
Total problems
10,025
1,540,000+
With CoT solutions
10,025
1,490,000+
With diagrams
6,093
740… See the full description on the dataset page: https://huggingface.co/datasets/a13905873166/China-K12-STEM-10K-CoT-Reasoning.aime25_amc23_gpqa_olymp_cotEpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-details
Dataset Card for Evaluation run of EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT
Dataset automatically created during the evaluation run of model EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-details.cot-oracle-reasoning-termination
cot-oracle-reasoning-termination
Mirror of japhba/cot-oracle-reasoning-termination for reproducibility (orig org fragile).
cot-oracle-eval-reasoning-termination-riya
CoT Oracle Eval: reasoning_termination_riya
Reasoning termination prediction — given a CoT prefix, predict whether the model will emit within the next 100 tokens. Labels are resampled (50 continuations per prefix): will_terminate if >=45/50 end within 20-60 tokens, will_continue if >=45/50 continue beyond 200 tokens. Includes Wilson CIs on resample counts. 50/50 balanced. Source: AI-MO/aimo-validation-aime + AI-MO/aimo-validation-amc (no overlap with GSM8K/MATH training data).
Part… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/cot-oracle-eval-reasoning-termination-riya.cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Quantom_Physics_And_Computing
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Quantom_Physics_And_Computing
データ件数: 2,862
平均トークン数: 1,110
最大トークン数: 2,334
合計トークン数: 3,175,666
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 15.5 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing.cot-oracle-reasoning-termination-balancedcleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Scientific_Discovery_and_Research
データ件数: 3,733
平均トークン数: 1,193
最大トークン数: 2,489
合計トークン数: 4,453,517
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 23.2 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research.EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO-details
Dataset Card for Evaluation run of EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO
Dataset automatically created during the evaluation run of model EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO-details.Quant-CoT-Factor-Reasoning-PreviewQuantitative Factor Generation: Chain-of-Thought (CoT) Trajectories
Dataset Description
This is a 100-episode preview of a proprietary Reinforcement Learning from Environment Feedback (RLEF) dataset. It is designed to fine-tune Large Language Models (LLMs) for institutional quantitative finance, specifically systematic factor discovery and vectorized Python execution.
The Architecture
The data captures multi-turn agentic loops where the LLM:
Formulates a cross-sectional equity factor… See the full description on the dataset page: https://huggingface.co/datasets/1Happy-neuron/Quant-CoT-Factor-Reasoning-Preview.cot-oracle-reasoning-termination