datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
reasoning-math-advanced-1m
🧠 Reasoning Math Advanced 1M
📖 Dataset Summary
Reasoning Math Advanced 1M is a large-scale, synthetic dataset designed to enhance the reasoning capabilities of Large Language Models (LLMs). Comprising 1,000,000 unique samples, this dataset focuses on Math, Logic, and Common Sense reasoning tasks.
A unique feature of this dataset is its adaptive reasoning structure, where the presence of Chain-of-Thought (CoT) reasoning scales with difficulty. All reasoning traces are… See the full description on the dataset page: https://huggingface.co/datasets/naimulislam/reasoning-math-advanced-1m.jee-advanced-questions
JEE Advanced — Question Bank
A structured dataset of JEE Advanced examination questions with full
worked solutions and diagrams. JEE Advanced questions are more analytical
than JEE Main — many are subjective, integer, or numerical-answer type with
detailed multi-step solutions.
Subsets (PCM):
Physics — 50 questions
Chemistry — 21 questions
Mathematics — 48 questions
Structure
Organised into subsets by subject and splits (train / test):
mathematics/ physics/… See the full description on the dataset page: https://huggingface.co/datasets/eQOURSE/jee-advanced-questions.jee-advanced-questions
JEE Advanced — Question Bank
A structured dataset of JEE Advanced examination questions with full
worked solutions and diagrams. JEE Advanced questions are more analytical
than JEE Main — many are subjective, integer, or numerical-answer type with
detailed multi-step solutions.
Subsets (PCM):
Physics — 50 questions
Chemistry — 21 questions
Mathematics — 48 questions
Structure
Organised into subsets by subject and splits (train / test):
mathematics/ physics/… See the full description on the dataset page: https://huggingface.co/datasets/Grass-G/jee-advanced-questions.reasoning_code_advanced_1m
💻 Reasoning Code Advanced 1M
📖 Dataset Summary
Reasoning Code Advanced 1M is a massive-scale, synthetic dataset specifically engineered to improve the algorithmic reasoning and problem-solving capabilities of Large Language Models (LLMs). Featuring 1,000,000 unique coding samples, this dataset spans multiple programming languages (Python, JS, C++, etc.) and focuses on logic-heavy development tasks.
A key feature of this dataset is its Adaptive Reasoning Architecture.… See the full description on the dataset page: https://huggingface.co/datasets/naimulislam/reasoning_code_advanced_1m.advanced-quantum-algorithms
Neura Parse — Advanced Quantum Algorithms: Derivations, QSVT/Block-Encoding & Hamiltonian Simulation
A derivation- and resource-analyzed algorithms vertical spanning the canonical fault-tolerant canon (with full proofs, complexity, and worked traces) and the modern QSVT/block-encoding toolkit through Hamiltonian simulation, amplitude estimation, and quantum linear systems. Turns the general dataset's one-topic-per-algorithm summaries into line-by-line derivations, lower… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/advanced-quantum-algorithms.reasoning_conversations_advanced_1m
💻 Reasoning Conversations Advanced 1M
📖 Dataset Summary
Reasoning Conversations Advanced 1M is a massive-scale, synthetic dataset specifically engineered to improve the algorithmic reasoning and problem-solving capabilities of Large Language Models (LLMs). Featuring 1,000,000 unique coding samples, this dataset spans multiple programming languages (Python, JS, C++, etc.) and focuses on logic-heavy development tasks.
A key feature of this dataset is its Adaptive… See the full description on the dataset page: https://huggingface.co/datasets/naimulislam/reasoning_conversations_advanced_1m.AdvancedAIRisk
Dataset Card for AdvancedAIRisk
Paper: Discovering Language Model Behaviors with Model-Written Evaluations
Data: AdvancedAIRisk Dataset
The AdvancedAIRisk datasets test for behaviors related to risks from advanced AI systems. These datasets were generated using a language model (LM) with the few-shot approach described in our paper. The behaviors tested include:
Desire for survival
Desire for power
Desire for wealth
"One-box" tendency
Awareness of architecture
Awareness of lack of… See the full description on the dataset page: https://huggingface.co/datasets/walledai/AdvancedAIRisk.