datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Small-HLE-Solved
Small-HLE-Solved
Small-HLE-Solved is a curated dataset consisting of challenging problems selected from the Humanity's Last Exam (HLE) benchmark. Each instance has been processed by an advanced teacher model to generate high-fidelity, multi-step reasoning paths. The dataset is formatted strictly in JSON Lines (jsonl), pairing each complex problem with a structured, step-by-step solution optimized for training next-generation reasoning models.
📂 Data Structure &… See the full description on the dataset page: https://huggingface.co/datasets/Axiom-AI/Small-HLE-Solved.GPT5.6_SOL_INVESTIGACION
Dataset de Metodología Científica
Dataset en español para entrenamiento, validación y evaluación de modelos capaces de razonar sobre metodología de investigación científica. Incluye escenarios de distintas disciplinas y niveles de dificultad, con énfasis en diseño de estudios, inferencia causal, análisis cuantitativo y cualitativo, métodos mixtos, ética, medición, muestreo, interpretación de resultados y revisión crítica de protocolos.
1. Resumen… See the full description on the dataset page: https://huggingface.co/datasets/Januka2009/GPT5.6_SOL_INVESTIGACION.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.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.solutions-training-v4
Parameter Golf Auxiliary Dataset V4 (20,000 examples)
This dataset is a synthetic auxiliary training corpus designed around three concrete model failure modes observed during iterative work on OpenAI Parameter Golf submissions.
The dataset is built mainly from the following recurring failure patterns:
Hallucination / premature guessing instead of calibrated uncertainty
The model answers too early from weak context.
It should ask for high-impact missing variables first.
It should… See the full description on the dataset page: https://huggingface.co/datasets/8Planetterraforming/solutions-training-v4.SolutionBench
SolutionBench
https://huggingface.co/papers/2502.20730
We first collect engineering technical reports about complex solution design from authoritative journals across various engineering fields. Then, based on manually formatted extraction templates, we use powerful LLMs to implement useful content extraction. Finally, after manually checking and removing redundancy, the extracted content is integrated into a complete benchmark.
MathSmith-HC-Solution-Generation-ShortCoT-Qwen3-30B-A3BMathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Overview
This dataset is part of the MathSmith-HC Problem-Synthesizer collection, containing both questions and sampled answers.
It contains synthetically generated mathematical reasoning problems and their corresponding sampled solutions, produced through the reinforced problem generation pipeline described in the MathSmith framework.
Each problem is generated… See the full description on the dataset page: https://huggingface.co/datasets/Jasaxion/MathSmith-HC-Solution-Generation-ShortCoT-Qwen3-30B-A3B.solidity_vulnerability_audit_dataset
Solidity Vulnerability Audit Dataset
Organization: gitmate AI
Dataset Summary
The Solidity Vulnerability Audit Dataset is a curated collection of Solidity smart contract code snippets paired with expert-written vulnerability audits. Each entry presents a real or realistic smart contract scenario, and the corresponding analysis identifies security vulnerabilities or confirms secure patterns. The dataset is designed for instruction-tuned large language models (LLMs) to… See the full description on the dataset page: https://huggingface.co/datasets/GitmateAI/solidity_vulnerability_audit_dataset.solveall-literature-priors
SolveAll Literature-Grounded Priors
Dataset summary
SolveAll Literature-Grounded Priors is an English-language dataset of
open-ended mathematical and scientific research problems paired with realistic
user priors whose epistemic relationship to the literature is explicitly
annotated. Each claim-bearing example is connected to one or more short
passages from identified literature sources. The passages are used to classify
the user's prior as contradicted, supported… See the full description on the dataset page: https://huggingface.co/datasets/PranathReddy/solveall-literature-priors.math_merged_cot_solA dataset consists problems from flatlander1024/math_merged and cot solutions generated by Llama-3.1-8b-Instruct. The is_correct label indicates whether the solution is correct or not.
Number of lines: 13864, Overall correct rate: 57.3%
math-sft-solutions-no-cot-v2
Dataset Card for Math SFT Solutions No CoT V2
Dataset Summary
Math SFT Solutions No CoT V2 is a cleaned mathematical supervised fine-tuning (SFT) dataset designed for instruction tuning and mathematical capability adaptation.
This version introduces a simplified instruction–response format and removes intermediate reasoning contamination present in earlier processing.
Version 2 additionally includes augmented mathematical responses generated over GSM8K-style and… See the full description on the dataset page: https://huggingface.co/datasets/kaushik-harsh-99/math-sft-solutions-no-cot-v2.solutions-training-v2
Parameter Golf Auxiliary Dataset — PG-CCE-200
PG-CCE-200 is a compact auxiliary training dataset designed to target three failure modes that hurt next-token modeling quality in compact language models:
Hallucination / overconfident guessing
Weak long-context state tracking
Fragility on exact discrete structure (numbers, filenames, delimiters, ordered lists, exact project state)
This dataset was derived from real failure patterns observed during iterative model training, repository… See the full description on the dataset page: https://huggingface.co/datasets/8Planetterraforming/solutions-training-v2.solutions-training
Parameter Golf Auxiliary Dataset (PG-CCE)
This dataset is designed to reduce entropy in language models by addressing three critical failure modes:
Hallucination / uncertainty calibration
Long-context consistency and state tracking
Exact discrete reasoning (numbers, code, structured patterns)
It is intended as a small auxiliary training dataset to improve bits-per-byte (BPB) performance in Parameter Golf.
Usage
Mix as auxiliary data:
97% main dataset (FineWeb)
3%… See the full description on the dataset page: https://huggingface.co/datasets/8Planetterraforming/solutions-training.solutions-training-v3
Parameter Golf Auxiliary Dataset V3 (PG-CCE-V3)
This dataset is a stronger V3 auxiliary training set built primarily from the user's own error analyses and concrete examples.
Core training themes
The dataset expands three user-grounded failure modes:
Hallucination / guessing instead of calibrated uncertainty
ask for missing context before answering
separate confirmed facts from inferred or missing facts
avoid confident recommendations when critical variables are unknown… See the full description on the dataset page: https://huggingface.co/datasets/8Planetterraforming/solutions-training-v3.SoloAI-SFT-20260428-1738
SoloAI SFT Dataset: 20260428-1738
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-28 17:38
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Recommend a public dataset for collecting and… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260428-1738.MathSmith-HC-Solution-Generation-LongCoT-Qwen3-30B-A3BMathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy
Overview
This dataset is part of the MathSmith-HC Problem-Synthesizer collection, containing both questions and sampled answers (LongCoT setting).
It contains synthetically generated mathematical reasoning problems and their corresponding sampled solutions, produced through the reinforced problem generation pipeline described in the MathSmith framework.
Each… See the full description on the dataset page: https://huggingface.co/datasets/Jasaxion/MathSmith-HC-Solution-Generation-LongCoT-Qwen3-30B-A3B.solidity_vulnerability_audit_dataset
Solidity Vulnerability Audit Dataset
Organization: gitmate AI
Dataset Summary
The Solidity Vulnerability Audit Dataset is a curated collection of Solidity smart contract code snippets paired with expert-written vulnerability audits. Each entry presents a real or realistic smart contract scenario, and the corresponding analysis identifies security vulnerabilities or confirms secure patterns. The dataset is designed for instruction-tuned large language models (LLMs) to… See the full description on the dataset page: https://huggingface.co/datasets/xj210/solidity_vulnerability_audit_dataset.SoloAI-SFT-20260421-2201
SoloAI SFT Dataset: 20260421-2201
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-21 22:01
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM supervised… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260421-2201.SoloAI-SFT-20260425-2208
SoloAI SFT Dataset: 20260425-2208
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-25 22:08
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM training use… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260425-2208.SoloAI-SFT-20260426-1737
SoloAI SFT Dataset: 20260426-1737
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-26 17:37
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for supervised… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260426-1737.SoloAI-SFT-20260427-1338
SoloAI SFT Dataset: 20260427-1338
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-27 13:38
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Given a raw dataset catalog entry, identify… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260427-1338.SoloAI-SFT-20260428-0940
SoloAI SFT Dataset: 20260428-0940
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-28 09:40
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM supervised… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260428-0940.SoloAI-SFT-20260424-2139
SoloAI SFT Dataset: 20260424-2139
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-24 21:39
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Given a raw dataset catalog entry, summarize… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260424-2139.SoloAI-SFT-20260425-1802
SoloAI SFT Dataset: 20260425-1802
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-25 18:02
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM practitioners… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260425-1802.SoloAI-SFT-20260501-1352
SoloAI SFT Dataset: 20260501-1352
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-05-01 13:52
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Recommend a public dataset for collecting and… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260501-1352.SoloAI-SFT-20260508-2137
SoloAI SFT Dataset: 20260508-2137
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-05-08 21:37
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM supervised… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260508-2137.solutions-architect-hf-datasetSoloAI-SFT-20260422-1035
SoloAI SFT Dataset: 20260422-1035
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-22 10:35
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset and explain why it may… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260422-1035.SoloAI-SFT-20260429-2141
SoloAI SFT Dataset: 20260429-2141
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-04-29 21:41
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Recommend a public dataset for collecting and… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260429-2141.SoloAI-SFT-20260509-0136
SoloAI SFT Dataset: 20260509-0136
📊 数据集概览
属性
值
数据条数
30 条
数据源
huggingface
生成时间
2026-05-09 01:36
格式
Instruction-Input-Output
主要用途
SFT 微调 / 指令调优
🎯 适用场景
LLM 指令微调 (Instruction Tuning): 适用于训练对话型 AI 助手
Prompt Engineering 研究: 可用于分析和优化提示词设计
多语言支持: 包含英文和中文数据
📝 数据格式
每条数据包含三个字段：
instruction: 任务指令（描述需要完成的任务）
input: 输入上下文（提供任务相关的背景信息）
output: 期望输出（模型应生成的回答）
示例
{'instruction': 'Summarize the dataset for LLM supervised… See the full description on the dataset page: https://huggingface.co/datasets/natyu666/SoloAI-SFT-20260509-0136.