datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hf-training-corpus
HF Training Corpus
Bulk-scraped multimodal training corpus: ~92,000 Hugging Face datasets streamed,
normalized and stored as per-dataset Parquet files under data/.
Every modality is captured: text, images (PNG bytes), audio (mono 16-bit WAV bytes),
video (bytes, capped) and tabular/timeseries (serialized to text).
Pipeline
Live crawl from a 92,458-ID corpus list (see
https://github.com/shadyuwugurl/hf-datasets-trees/blob/main/datasets.txt)
streaming=True reads… See the full description on the dataset page: https://huggingface.co/datasets/MC7ever/hf-training-corpus.GUI-World
GUI-World: A Dataset for GUI-Orientated Multimodal Large Language Models
Dataset: GUI-World
Overview
GUI-World introduces a comprehensive benchmark for evaluating MLLMs in dynamic and complex GUI environments. It features extensive annotations covering six GUI scenarios and eight types of GUI-oriented questions. The dataset assesses state-of-the-art ImageLLMs and VideoLLMs, highlighting their limitations in handling dynamic and multi-step tasks. It provides… See the full description on the dataset page: https://huggingface.co/datasets/ONE-Lab/GUI-World.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K.shofo-tiktok-general-small
Shofo TikTok General (Small)
Overview
Shofo TikTok General (Small) is a dataset containing 50,000 TikTok videos with comprehensive metadata, transcripts, comments, and engagement metrics. This is a curated subset of Shofo's larger TikTok index, which contains hundreds of millions of indexed videos.
Size: ~50K videos (~500GB)
Modality: Video + Audio + Text (transcripts, comments, captions)
Source: TikTok
Schema
Column
Type
Description
file_name… See the full description on the dataset page: https://huggingface.co/datasets/Shofo/shofo-tiktok-general-small.VISTA
VISTA Dataset
Dataset Structure
dataset/
├── videos/ # Video files directory
│ ├── train_part1/ # Training set videos (first 8000 samples)
│ ├── train_part2/ # Training set videos (remaining samples)
│ ├── val/ # Validation set videos
│ └── test/ # Test set videos
├── train_part1.json # Training set metadata (first 8000 samples)
├── train_part2.json # Training set metadata (remaining samples)… See the full description on the dataset page: https://huggingface.co/datasets/dongqi-me/VISTA.OmniVideoBench
OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs
✨ Overview
Recent advances in multimodal large language models (MLLMs) have brought remarkable progress in video understanding.However, most existing benchmarks fail to jointly evaluate both audio and visual reasoning — often focusing on one modality or overlooking their interaction.
🎬 OmniVideoBench fills this gap.It’s a large-scale, rigorously curated… See the full description on the dataset page: https://huggingface.co/datasets/NJU-LINK/OmniVideoBench.rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/rtx-5090-benchmarks.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.MERCI-plus
MERCI — Multimodal Emotionally-aware Robot Dialogue Corpus
Real-robot open-domain dialogues collected with the PERCY conversational system on the ARI social robot.
Each session includes synchronized video (3 views), audio, transcripts, visual emotion (FER), text sentiment (VADER), and persona metadata. This release adds a turn-level cross-modal audit and two indexing benchmarks from our journal extension of CBMI 2025 MERCI.
Count
Sessions
41
Dialogue turns (user +… See the full description on the dataset page: https://huggingface.co/datasets/zhijin-meng/MERCI-plus.donald-trump-truth-social-posts
Donald Trump Truth Social Posts Archive
Archive overview
36,170 public Truth Social posts associated with Donald J. Trump's @realDonaldTrump account. The release preserves source URLs, timestamps, post types, original HTML, extracted plain text, attachment provenance, and analysis-ready tables.
It also includes streamable image media plus video metadata and transcripts where the source provides them.
The package is source-linked and reconciled by archive ID.… See the full description on the dataset page: https://huggingface.co/datasets/Cameronk199/donald-trump-truth-social-posts.Audio-Video-Engineering-Agentic-Tasks-1M
Audio/Video Engineering Agentic Tasks (1M)
Abstract
A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M.SWITCH
SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios
[arXiv]
[leaderboard]
[dataset]
[PDF]
Dataset Summary
SWITCH (Semantic World Interface Tasks for Control & Handling) is a multimodal embodied-interaction benchmark for understanding, modeling, and evaluating actions over Tangible Control Interfaces (TCIs) in egocentric real-world scenarios.
TCIs include everyday interfaces such as appliance panels, lighting… See the full description on the dataset page: https://huggingface.co/datasets/BAAI-Agents/SWITCH.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/rAVEUK/Creative-Professionals-Agentic-Tasks-1M.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/BlueIsGreen/Edge-Agent-Reasoning-WebSearch-260K.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/Torenn/Edge-Agent-Reasoning-WebSearch-260K.incantation-elden-ring-scenes
Incantation Elden Ring Combat Captions
Paper | Project page | GitHub
Preview subset. This repository is a public preview and reference subset of the Incantation dataset. It documents the data format, annotation style, and initial training material used by the project. It should not be interpreted as the final dataset, the complete benchmark, or the full data scale used by the paper.
This dataset contains manually collected Elden Ring combat clips paired with structured… See the full description on the dataset page: https://huggingface.co/datasets/zhush/incantation-elden-ring-scenes.edge-agent-reasoning-websearch-260k
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/ppenner/edge-agent-reasoning-websearch-260k.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/DEMIRUNC/Edge-Agent-Reasoning-WebSearch-260K.LAMBDA
Dataset Summary
LAMDBA is a long term ad memorability dataset, featuring data from 1749 participants and 2205 ads across 276 brands.
Dataset Structure
from datasets import load_dataset
ds = load_dataset("behavior-in-the-wild/LAMBDA")
ds
DatasetDict({
train: Dataset({
features: ['video_id', 'recall_score', 'youtube_id', 'ad_details'],
num_rows: 1964
})
test: Dataset({
features: ['video_id', 'recall_score', 'youtube_id', 'ad_details']… See the full description on the dataset page: https://huggingface.co/datasets/behavior-in-the-wild/LAMBDA.synwts
SynWTS: Synthetic Woven Traffic Safety Dataset
SynWTS is a high-fidelity synthetic dataset built as a Digital Twin of the Woven Traffic Safety (WTS) dataset. It is developed for the 2026 AI City Challenge (Track 2) to advance Sim2Real research in transportation safety understanding.
Dataset Summary
Participants in the Sim2Real challenge must train models exclusively on this synthetic data and evaluate performance on real-world video. SynWTS provides a geometric… See the full description on the dataset page: https://huggingface.co/datasets/mlcglab/synwts.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/kryp1234/Creative-Professionals-Agentic-Tasks-1M.rtx-5090-benchmarks
RTX 5090 LLM Benchmarks
Speed and quality benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with llm-bench-rig.
Quality Benchmarks
Generative evaluation through llama-server chat completions. Replicates standard benchmark methodology using custom evaluators — no lm-evaluation-harness dependency.
Results are split by reasoning mode: comparing a thinking-on (reasoning) model's quality against a thinking-off model is apples-to-oranges, so the two groups… See the full description on the dataset page: https://huggingface.co/datasets/omegaprime669/rtx-5090-benchmarks.OmniRewardBench
Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences
📄 Paper |
💻 Code |
🤗 Benchmark (This Dataset) |
🤗 Training Data |
🤗 Model |
🏠 Homepage
Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainly focused on text and image modalities, offering limited support for video, audio, and other modalities; and… See the full description on the dataset page: https://huggingface.co/datasets/HongbangYuan/OmniRewardBench.MixEval-X
🚀 Project Page | 📜 arXiv | 👨💻 Github | 🏆 Leaderboard | 📝 blog | 🤗 HF Paper | 𝕏 Twitter
MixEval-X encompasses eight input-output modality combinations and can be further extended. Its data points reflect real-world task distributions. The last grid presents the scores of frontier organizations’ flagship models on MixEval-X, normalized to a 0-100 scale, with MMG tasks using win rates instead of Elo. Section C of the paper presents example data samples and model responses.… See the full description on the dataset page: https://huggingface.co/datasets/MixEval/MixEval-X.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/JACKYS999/Edge-Agent-Reasoning-WebSearch-260K.AutoCaption
🏷️ AutoCaption
📄 Paper: Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search
🧠 GitHub: AutoCaption
This repository provides the SFT training data and MCTS-VCB evaluation benchmark generated by the AutoCaption framework.
📦 Dataset Summary
This dataset contains 11,184 total samples across 2 subsets:
sft_data – for supervised fine-tuning of caption models
mcts_vcb – for evaluation using MCTS-generated captions and keypoints… See the full description on the dataset page: https://huggingface.co/datasets/HasuerYu/AutoCaption.Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/kanepi-1977/Agent-Reasoning-WebSearch-260K.incantation-elden-ring-scenes
Incantation Elden Ring Combat Captions
Preview subset. This repository is an early public preview and reference subset of the Incantation dataset. It is provided to document the data format, annotation style, and initial training material ahead of the full paper/project release. It should not be interpreted as the final dataset, the complete benchmark, or the full data scale used by the paper.
This dataset contains manually collected Elden Ring combat clips paired with structured… See the full description on the dataset page: https://huggingface.co/datasets/MatrixTeam/incantation-elden-ring-scenes.simpsons_infoThe information of all episodes of the cartoon show "The Simpsons" from wikipedia. Some (mainly in recent 32, 33, 34 seasons) plot missing.
MT-Video-Bench
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues
✨ Introduction
Recent advances in multimodal large language models (MLLMs) have brought remarkable progress in video understanding.However, existing evaluation benchmarks remain limited to single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios.
🎬 MT-Video-Bench fills this… See the full description on the dataset page: https://huggingface.co/datasets/NJU-LINK/MT-Video-Bench.