datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.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.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.
synthetic-enterprise-ops-pack-sample
Solstice Synthetic Enterprise Operations Pack (Sample)
A multi-system graph dataset for agent evaluation and RAG benchmarking. This dataset simulates the interconnected operations of a modern technology company, linking sales activities, engineering workflows, IT support, and internal communications.
Built by Solstice AI Studio as a free sample of a larger commercial pack. 100% synthetic — no real company or employee data.
What's in the box
This dataset consists of 32… See the full description on the dataset page: https://huggingface.co/datasets/solsticestudioai/synthetic-enterprise-ops-pack-sample.organic-chemistry-synthesis-planning-corpus
Organic Chemistry Synthesis Planning Corpus
Status: actively ingesting. A comprehensive reaction backbone is already uploaded
(millions of reactions; see Ingested data below). Curation and
additional sources are ongoing. See Roadmap.
Quickstart (for students / first-time users)
You need a free Hugging Face account, and to accept this dataset's terms on its page (it's gated).
pip install datasets transformers
huggingface-cli login
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/Solshine/organic-chemistry-synthesis-planning-corpus.nanochat-depo-composition-depth2-w4-retry-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
nanochat-depo-l0-symbolic-20260715
Nanochat Depo-L0: symbolic
This is a diagnostic, separately versioned Depo source. Each row contains one
16-node cycle and eight queries at depths 1, 2, 4, and 8. Only the eight
single-letter answers and terminal token are supervised. It is designed for a
one-document-per-sequence training protocol and must not be treated as public
Depo v3 data.
nanochat-world-state-v2-49k-20260714
Nanochat World-State Tracking Capability
Each deterministic latent world describes initial people, rooms, portable objects,
containers, and fixed surfaces followed by a valid chronological event sequence.
The task asks for one exact final location, holder, container, or support. Six
natural renderer styles appear in training; validation and test may use all eight.
The exact state replay engine supplies every answer. No language model generated
or labeled the data. Targeted-v2… See the full description on the dataset page: https://huggingface.co/datasets/SolidSnake123/nanochat-world-state-v2-49k-20260714.nanochat-depo-l0-depth1-curriculum-20260715
Nanochat Depo-L0: symbolic
This is a diagnostic, separately versioned Depo source. Each row contains one
16-node cycle and eight queries under the depth1_only schedule. Only the eight
single-letter answers and terminal token are supervised. It is designed for a
one-document-per-sequence training protocol and must not be treated as public
Depo v3 data.
nanochat-depo-retrieval-width4-20260715
Nanochat Depo retrieval v1
Each latent 16-node graph yields eight independent, token-aligned, depth-one
query documents. This arm exposes 4 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.
nanochat-depo-composition-depth2-w4-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
nanochat-depo-composition-depth2-w4-transport-20260715
Nanochat Depo composition v1
Each 16-node single-cycle graph yields eight independent one-query documents:
four base starts paired across query depths (1, 2).
This source contains train and validation splits only. Phase depth is
2; the materialized context width is 4.
onlybrains-reasoning-10k
OnlyBrains Multi-Domain Dataset (11.7K)
Structured traces from 5 interconnected projects across 8 data domains. Generated by the KCC (Konomi Cube Coin) ecosystem — every trace was mined as a $KONO block on a Proof-of-Useful-Work blockchain.
Domains
Source
Domain
Count
Description
reason
broly
10,000
Structured reasoning (CoT, ToT, GoT, BoT, SelfAsk, ReAct, Reflexion)
reason
broly
1,000
Real reasoning traces with full step data
widget
kp2p
490
P2P widget UDT… See the full description on the dataset page: https://huggingface.co/datasets/SolarTesla/onlybrains-reasoning-10k.