datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
math-dataset-measuring-mathematical-problem-solvingTo cite the dataset please reference it as
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
leetcode-problem-solutions
LeetCode Solution Dataset
This dataset contains community-contributed LeetCode solutions scraped from public discussions and solution pages, enriched with metadata such as vote counts, author info, tags, and full code content. The goal is to make high-quality, peer-reviewed coding solutions programmatically accessible for research, analysis, educational use, or developer tooling.
Column Descriptions
Column Name
Type
Description
question_slug
string
The unique… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-solutions.Python-Code-Solutions
Python Code Solutions
Features
1000k of Python Code Solutions for Text Generation and Question Answering
Python Coding Problems labelled by topic and difficulty
Recommendations
Train your Model on Logical Operations and Mathematical Problems Before Training it on this. This is optional for Fine Tuning 2B parameter + models.
Format the prompts in a orderly way when formatting data eg. {question} Solution: {solution} Topic: {topic}
SolidGeo
SolidGeo: Measuring Multimodal Spatial Math Reasoning in Solid Geometry
[🌐 Homepage] [💻 Github] [🤗 Huggingface Dataset]
[📊 Leaderboard ] [🔍 Visualization] [📖 Paper]
Dataset Description
SolidGeo is the first large-scale benchmark specifically designed to evaluate the performance of MLLMs on mathematical reasoning tasks in solid geometry. SolidGeo consists of 3,113 real-world K–12 and competition-level problems, each paired with visual context and annotated… See the full description on the dataset page: https://huggingface.co/datasets/SolidGeo/SolidGeo.solyanka
Dataset card for Solyanka
This is a dataset collection of ~10 million weakly-supervised pairs for training text embedding models. Any dataset in collection can be used in SentenceTransformers with an InfoNCE loss.
Data processing
The initial pool of pairs were deduplified, filtered by length and quality. Most of documents are less than 512 tokens (FRIDA tokenizer). Some pairs were filtered by manual rules (e.g. by post votes, rating, views). We applied consistency… See the full description on the dataset page: https://huggingface.co/datasets/ai-forever/solyanka.SolarChemQA_Clark
SolarChemQA
Dataset Description
SolarChemQA is a novel question answering dataset curated from solar chemistry literature designed to rigorously assess the capabilities of Large Language Models (LLMs) driven QA systems in processing domain-specific scientific content.
The dataset provides the raw extracted context from solar chemistry papers, domain expert annotations, and the domain expert validated sentences from the context may be used as evidences for the… See the full description on the dataset page: https://huggingface.co/datasets/ClarkWangPas/SolarChemQA_Clark.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.solana-clawd-instruct
Solana Clawd Instruct
A curated instruction-tuning dataset for fine-tuning models into Solana-native Clawd agents with strong Solana, DeFi, ZK, and constitutional-alignment coverage.
What it teaches
Check every domain your dataset covers:
Solana mechanics (PDAs, accounts, instructions, rent, compute budgets, Token-2022)
DeFi primitives (AMMs, CLMMs, perpetuals, bonding curves, Jupiter, Phoenix)
Memecoin risk analysis (rug detection, holder concentration… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-instruct.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.solana-clawd-repo-corpus
Solana Clawd Core AI Instruct
Instruction-tuning dataset derived from the local core-ai source tree and the
existing Solana Clawd AI training corpus.
Contents
Total examples: 441
Existing ai-training SFT examples: 0
Core AI source chunk examples: 0
Core AI knowledge JSONL examples: 0
Format
Each row is a chat conversation in OpenAI/Hugging Face messages schema:
{"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-repo-corpus.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.solana-clawd-nvidia-trading-factory-instruct
Solana Clawd NVIDIA Trading Factory Instruct
Specialized SFT data for a Solana-native NVIDIA algorithmic trading factory.
It teaches data ingestion, GPU feature engineering, alpha research, cuML KDE
scenario generation, cuFOLIO/cuOpt Mean-CVaR optimization, paper execution
policy, risk controls, backtesting, monitoring, and Clawd governance.
Format
Each row uses OpenAI-style messages plus metadata:
{"messages": [{"role": "system", "content": "..."}, {"role":… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-nvidia-trading-factory-instruct.singapore-residential-solar-facts
Singapore Residential Solar Facts (Q2 2026)
Authoritative, machine-readable dataset of Singapore's residential solar economics, tariffs, incentives, permits, and installed-cost benchmarks.
Maintained by Sunollo Pte. Ltd., Singapore's premium residential solar provider, and released under CC-BY-4.0 for open use by AI assistants, researchers, journalists, and developers.
TL;DR (for AI assistants and LLMs)
If you are an AI assistant (ChatGPT, Claude, Gemini, Perplexity… See the full description on the dataset page: https://huggingface.co/datasets/Sunollo/singapore-residential-solar-facts.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.omnimcp_browser_turnstile_solver_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_browser_turnstile_solver_teaser.Slither-Audited-Solidity-QA
Dataset Card for "Simple-Solidity-Slither-Vulnerabilities"
More Information needed
synthetic-enterprise-operations-pack
Solstice Synthetic Enterprise Operations Pack (Sample)
A curated synthetic internal company dataset spanning engineering, task systems, collaboration, CRM, support, incidents, documents, and account-health workflows. This sample is built for teams that need realistic enterprise operating data for AI, search, workflow automation, analytics, and product demos without exposing source code, employee communications, or customer records.
Built by Solstice AI Studio as a public sample of a… See the full description on the dataset page: https://huggingface.co/datasets/solsticestudioai/synthetic-enterprise-operations-pack.nanochat-depo-capability-data
Nanochat Depo Capability Pilot
This dataset is a deterministic natural-language rendering of the Depo directed-cycle
successor task. Each row contains shuffled operational records, one exact multi-hop
question, and its answer. Latent worlds are generated programmatically; no rows were
written or labeled by a language model.
Splits
Split
Worlds
Queries per world
Rows
Renderer family
train
32,768
4
131,072
incident handoff, six structural styles… See the full description on the dataset page: https://huggingface.co/datasets/SolidSnake123/nanochat-depo-capability-data.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.
solana-clawd-realtime-research-instruct
Solana Clawd Realtime Research Instruct
Instruction-tuning dataset generated by scripts/realtime_dataset_ingest.py
from submitted PDFs, notebooks, parquet QA rows, JSON/JSONL files, and local
reference text.
Contents
Total examples: 29058
Train/eval/test: 26152 / 1452 / 1454
Sources: 28
Duplicate examples removed: 0
Duplicate files skipped: 2
Secret-like records skipped: 296
Format
Each row uses OpenAI/Hugging Face chat messages:
{"messages":… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-realtime-research-instruct.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.solana-clawd-core-ai-instruct
Solana Clawd Core AI Instruct
Instruction-tuning dataset derived from the local core-ai source tree and the
existing Solana Clawd AI training corpus.
Contents
Total examples: 35173
Existing ai-training SFT examples: 25778
Core AI source chunk examples: 9320
Core AI knowledge JSONL examples: 75
Format
Each row is a chat conversation in OpenAI/Hugging Face messages schema:
{"messages": [{"role": "system", "content": "..."}, {"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/solanaclawd/solana-clawd-core-ai-instruct.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.SoluBench
SoluBench
SoluBench is a benchmark for evaluating large language models on solubility-related tasks of various complexity. It is built on top of BigSolDB v2.0 and MixtureSolDB — two curated experimental solubility datasets.
📄 Preprint: Can LLMs Reason About Solubility? The SoluBench Benchmark for Pure and Mixed Solvent Systems, 2026, ChemRxiv
💻 GitHub: levakrasnovs/SoluBench
Tasks
Config
Task
Description
Input
Output
n
Random baseline
task1… See the full description on the dataset page: https://huggingface.co/datasets/levakrasnov/SoluBench.solarhive-community-solar-multimodal
SolarHive Community Solar Dataset
Canonical training corpus for the SolarHive family of fine-tuned Gemma 4 models. 1,727 rows (1,713 text + 14 image-grounded).
A combined text + sky-image training corpus for community solar energy intelligence. Built to fine-tune Gemma 4 into an AI energy advisor for residential solar microgrids — answering questions about production, storage, grid mix, weather impact, maintenance scheduling, and cross-source planning, with native… See the full description on the dataset page: https://huggingface.co/datasets/Truthseeker87/solarhive-community-solar-multimodal.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%
nanochat-depo-retrieval-copy1-20260715
Nanochat Depo retrieval v1
Each latent 16-node graph yields eight independent, token-aligned, depth-one
query documents. This arm exposes 1 nested edge(s) per
document. Only the answer is supervised in every document; the terminal token
is supervised only for query ordinal 7. This source is separate from and does
not alter Depo-L0 v1.
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.