datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Time-300B
Dataset Card for Time-300B
This repository contains the Time-300B dataset of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
For details on how to use this dataset, please visit our GitHub page.
llmtcl
⚡ LitGPT
20+ high-performance LLMs with recipes to pretrain, finetune, and deploy at scale.
✅ From scratch implementations ✅ No abstractions ✅ Beginner friendly
✅ Flash attention ✅ FSDP ✅ LoRA, QLoRA, Adapter
✅ Reduce GPU memory (fp4/8/16/32) ✅ 1-1000+ GPUs/TPUs ✅ 20+ LLMs
Quick start •
Models •
Finetune •
Deploy •
All workflows •
Features •
Recipes (YAML) •
Lightning AI •
Tutorials… See the full description on the dataset page: https://huggingface.co/datasets/Maple222/llmtcl.mapleMAPLE
MAPLE: Multi-Aspect Full-Paper Scientific Retrieval Benchmark
MAPLE is an expert-validated benchmark for multi-aspect full-paper retrieval. It contains 2,095 fine-grained queries derived from 210 recent machine learning papers, together with a retrieval corpus of 73,973 candidate papers. Each target paper is paired with multiple queries grounded in textual or multimodal evidence and covering different aspects of the paper, including motivation, method, and experimental findings.… See the full description on the dataset page: https://huggingface.co/datasets/kai-02/MAPLE.UniREditBench-ResultsOpenstoryPlusPlus
Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
We introduce OpenStory++, a large-scale open-domain dataset contains focusing on enabling MLLMs to perform storytelling generation tasks.
related resorcce
paper: https://arxiv.org/abs/2408.03695
code: https://github.com/YeLuoSuiYou/openstorypp
News
2024/7/31 We have reorganized and distributed the high-quality subset and released most of the story data collected… See the full description on the dataset page: https://huggingface.co/datasets/MAPLE-WestLake-AIGC/OpenstoryPlusPlus.UniREdit-Data-100KUniREditBench: A Unified Reasoning-based Image Editing Benchmark
MAPLE_40K
MAPLE_40K — 1024 × 1024
40,000 synthetic object-editing pairs derived from OBJect-3DIT / allenai/object-edit: 20,000 rotation and 20,000 translation pairs. All images are 1024 × 1024. This version replaces the previous 256 × 256 release on main.
Source and target RGB images were enhanced from native 256 × 256 using SeedVR2-7B FP16 weights, fixed seed 42, LAB color correction, and three low-frequency consistency iterations. This is model-based super-resolution. Fine details are… See the full description on the dataset page: https://huggingface.co/datasets/zhenghuayu/MAPLE_40K.maple-collections-hackathon
Maple Bank Collections Hackathon dataset (fully synthetic)
Dataset for the CIBC Collections Hackathon build phase. One fictional bank ("Maple Bank"), 1,000,000 customers
(1,020,000 CRM records), October 2016 to September 2026, snapshot date 2026-09-28. Every person, account, call,
recording and document is synthetic.
Files
File
Size
What
maple_collections_release.zip
see file list
Start here. 31 tables (CSV + Parquet), transcripts (JSON), policy… See the full description on the dataset page: https://huggingface.co/datasets/nuxsh/maple-collections-hackathon.UniREditBenchUniREditBench: A Unified Reasoning-based Image Editing Benchmark
maple
Overview
Maple is an open-source full-stack code dataset developed and released by Tudor Iustin.
It is designed to support code generation, web development, supervised fine-tuning, instruction tuning, post-training, dataset research, and evaluation workflows for code-capable AI systems.
Maple contains 16,000 full-stack code samples totaling approximately 102 million tokens. It focuses on realistic software-building tasks, including web applications, product interfaces… See the full description on the dataset page: https://huggingface.co/datasets/tudor-iustin22/maple.maple728-time_300B
Dataset Card for Time-300B
This repository contains the Time-300B dataset of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
For details on how to use this dataset, please visit our GitHub page.
maple-preview-cuda-benchmarks
Maple Preview TQ2_0 CUDA Benchmarks
Reproducibility data for the TQ2_0 CUDA patches in
PascalAI2024/maple-preview-windows-cuda.
This repository contains benchmark data, patch files, hashes, and raw validation
evidence. It does not duplicate the Maple model weights.
Result
The fresh local A/B/B/A validation on an RTX 4080 SUPER reproduced the fused-MMQ
prompt-processing gain:
Variant
pp512 mean
pp512 median
tg128 mean
tg128 median
Correctness
MMQ enabled… See the full description on the dataset page: https://huggingface.co/datasets/x0me/maple-preview-cuda-benchmarks.MAPLE-Lua-Corpusmaple
MAPLE (Bill Summarization, Tagging, Explanation)
In this project, we generate summaries and category tags for of Massachusetts bills for MAPLE Platform. The goal is to simplify the legal language and content to make it comprehensible for a broader audience (9th-grade comprehension level) by exploring different ML and LLM services.
This repository contains a pipeline from taking bills from Massachusetts legislature, generating summaries and category tags leveraging different the… See the full description on the dataset page: https://huggingface.co/datasets/ayang903/maple.escarpment-lab-data
Escarpment Retreat Evolution Teaching Dataset
This public dataset contains the precomputed display assets for an educational
web platform about fluvial-erosion-driven escarpment retreat.
Contents
81 MATLAB/TopoToolbox/TTLEM parameter scenarios
161 time steps per scenario from 0 to 32 Myr
Corrected top-down plan views
Fixed-perspective three-dimensional views
Basin masks, river-profile data, knickpoints, and scenario metadata
The web release contains 39,368 files… See the full description on the dataset page: https://huggingface.co/datasets/maplestang/escarpment-lab-data.MAPLE-bench
MAPLE Benchmark Test Splits
This repository contains the test splits for the MAPLE benchmark introduced in the paper MAPLE: Modality-Aware Post-training and Learning Ecosystem (https://arxiv.org/pdf/2602.11596). The benchmark is designed for modality-aware multimodal evaluation under different required-signal settings, where each sample is annotated with the minimal modality subset needed to solve the task.
Dataset Overview
MAPLE-bench evaluates multimodal reasoning… See the full description on the dataset page: https://huggingface.co/datasets/lihVerma/MAPLE-bench.maplestory_characters_hdMME-RealWorld
2024.11.14 🌟 MME-RealWorld now has a lite version (50 samples per task) for inference acceleration, which is also supported by VLMEvalKit and Lmms-eval.
2024.10.27 🌟 LLaVA-OV currently ranks first on our leaderboard, but its overall accuracy remains below 55%, see our leaderboard for the detail.
2024.09.03 🌟 MME-RealWorld is now supported in the VLMEvalKit and Lmms-eval repository, enabling one-click evaluation—give it a try!"
2024.08.20 🌟 We are very proud to launch MME-RealWorld, which… See the full description on the dataset page: https://huggingface.co/datasets/Mapleyuchen/MME-RealWorld.maplemaplestory-worlds-creator-qa
MapleStory Worlds Creator QA
Synthetic question-answer dataset built from the official
MapleStory Worlds Creator Center
documentation. Questions are generated to be self-contained and grounded in the
source docs; answers avoid source/meta references so they read like an expert
explanation. Some QA pairs are composed from multiple related documents
(see combo_sources).
Parallel Korean/English. Intended for instruction tuning, QA, and retrieval.
Composition… See the full description on the dataset page: https://huggingface.co/datasets/msw-ai-tf/maplestory-worlds-creator-qa.maple-personas
MAPLE-Personas: A Benchmark for Evaluating Personalized Conversational AI
A dataset for evaluating how well conversational AI systems learn and apply user preferences from natural dialogue. This benchmark accompanies the MAPLE (Memory-Adaptive Personalized LEarning) framework.
Dataset Description
This dataset tests an AI assistant's ability to implicitly learn user traits from conversation context and apply that knowledge to personalize responses to open-ended… See the full description on the dataset page: https://huggingface.co/datasets/prdeepakbabu/maple-personas.maple-analyst-cap-sft-data
maple-analyst-cap-sft-data
Dataset de SFT para fine-tune de maple-analyst-cap-bf16 (Qwen3.5-MoE 20.2B
ternario). 4,956 trazas de razonamiento (pseudothinking + answer) en formato
TC (ThinkingCap).
Composición
Fuente
Filas
thinkingcap (curriculum, trazas bigbang)
1,782
openmle-condensed (FrontisAI OpenMLE-SFT-Traces, condensadas con distiller LFM2.5-2.6B q8_0)
702
bigbang_mmlu
508
bigbang_bbh
441
hermes_function_calling
360
aya_dataset
342… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/maple-analyst-cap-sft-data.DynaSolidGeo-SamplePaper: https://arxiv.org/abs/2510.22340
Github Repo: https://github.com/ChangtiWu/DynaSolidGeo
In the "Appendix.E: DynaSolidGeo as a Training Dataset" in our paper, we sample K = 10 batches of instances using random seeds from 0 to 9, resulting in a total of 5,030 samples.
These samples are divided into a training set (3,627 samples), a validation set (403 samples), and a test set (1,000 samples).
Maple-Routing-Observationsmarin-starcoderdata_maplemaple
Overview
Maple is an open-source full-stack code dataset developed and released by Fabric AI.
It is designed to support code generation, web development, supervised fine-tuning, instruction tuning, post-training, dataset research, and evaluation workflows for code-capable AI systems.
Maple contains 16,000 full-stack code samples totaling approximately 102 million tokens. It focuses on realistic software-building tasks, including web applications, product interfaces, dashboards… See the full description on the dataset page: https://huggingface.co/datasets/FabricAI/maple.maplestory_captchaA huge collection of English MapleStory's captcha text in jpg that I have collected over the years. ENJOY!!
It us used by pre-Big Bang MapleStory, throughout the game from Lie-Detector (anti-macro item), logins, to NPC conversations.
Up till version 190 when they have switched using Runes (Up, Down, Left, Right arrow keys) for most of the time for detection of macros and bots.
These images are not labelled, I'm releasing this for anyone that wants the dataset to be able to train a model… See the full description on the dataset page: https://huggingface.co/datasets/lastbattle/maplestory_captcha.canada-china-trade
Canada-China B2B Trade Dataset
Dataset Description
A curated dataset of Canada-China bilateral trade statistics, commodity breakdowns, provincial data, and B2B sourcing knowledge for use in AI/LLM research and applications.
Maintained by: MapleBridge.io — AI-powered B2B matching platform for Canada-China trade.
Dataset Contents
File
Description
Rows
canada_china_trade_annual.csv
Annual bilateral trade volume 2015-2024 (CAD billions)
10… See the full description on the dataset page: https://huggingface.co/datasets/maplebridge/canada-china-trade.MapleStory_Monsters_Dataset