Model Hub

TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. Quick Links Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.

LLaVA-OneVision-1.5 Instruction Data Paper | Code 📌 Introduction This dataset, LLaVA-OneVision-1.5-Instruct, was collected and integrated during the development of LLaVA-OneVision-1.5. LLaVA-OneVision-1.5 is a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. This meticulously curated 22M instruction dataset (LLaVA-OneVision-1.5-Instruct) is part of a comprehensive and… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data.

Dataset Card for "ag_news" Dataset Summary AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml… See the full description on the dataset page: https://huggingface.co/datasets/fancyzhx/ag_news.

OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the Llama3.1-405B-Instruct model. The training set problems of GSM8K and MATH are used for constructing the dataset in the following ways: Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.

Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.

This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model. It contains 21M passages from wikipedia along with their DPR embeddings. The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.

Fine Vision FineVision is a massive collection of datasets with 17.3M images, 24.3M samples, 88.9M turns, and 9.5B answer tokens, designed for training state-of-the-art open Vision-Language-Models. More detail can be found in the blog post: https://huggingface.co/spaces/HuggingFaceM4/FineVision The version in this repository concatenated all the configs in the original dataset and then shuffled them. This is done to facilitate streaming the data directly from the hub! Load… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/FineVisionMax.

Forecast News Deduplicated daily news corpus used by forecast-sim and future-sim. Snapshot 31,696,086 articles 3,401 daily partitions Coverage: 2016-08-26 through 2026-06-30 Snapshot published: 2026-07-13 Stored data size: approximately 168.8 GB The Parquet files are the canonical complete representation. The repository also contains daily JSONL files where available and compact headline JSON files used by article-browsing workflows. Layout Files… See the full description on the dataset page: https://huggingface.co/datasets/shash42/forecast-news.

🚀 AutoMathText-V2: A 2.46 Trillion Token AI-Curated STEM Pretraining Dataset   🎉 AutoMathText-v2 has surpassed 1.5 million downloads! We'd love to know how you're using it. Please take 1 minute to fill out our use case survey. Your feedback will directly shape the future roadmap of this dataset.👉 Share your use case here 📊 AutoMathText-V2 consists of 2.46 trillion tokens of high-quality, deduplicated text spanning web content, mathematics, code, reasoning, and… See the full description on the dataset page: https://huggingface.co/datasets/OpenSQZ/AutoMathText-V2.

Dataset Card for "ArtifactAI/arxiv_s2orc_parsed" Dataset Description https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed Dataset Summary AlgorithmicResearchGroup/arxiv_s2orc_parsed is a subset of the AllenAI S2ORC dataset, a general-purpose corpus for NLP and text mining research over scientific papers, The dataset is filtered strictly for ArXiv papers, including the full text for each paper. Github links have been extracted from each… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed.

GPT-Image-Edit-1.5M A Million-Scale, GPT-Generated Image Dataset 📃Arxiv | 🌐 Project Page | 💻Github GPT-Image-Edit-1.5M is a comprehensive image editing dataset that is built upon HQ-Edit, UltraEdit, OmniEdit and Complex-Edit, with all output images regenerated with GPT-Image-1. 📣 News [2025.08.20] 🚀 We provide a script for multi-process downloading. See Multi-process Download. [2025.07.27] 🤗 We release GPT-Image-Edit, a state-of-the-art image editing model with… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/GPT-Image-Edit-1.5M.

TL;DR of L2D, the world's largest self-driving dataset! Read more about L2D on the official Huggingface blog: LeRobot goes to driving school 90+ TeraBytes of multimodal data (5000+ hours of driving) from 30 cities in Germany 6x surrounding HD cameras and complete vehicle state: Speed/Heading/GPS/IMU Continuous: Gas/Brake/Steering and discrete actions: Gear/Turn Signals Environment state: Lane count, Road type (highway|residential), Road surface (asphalt, cobbled, sett), Max speed limit.… See the full description on the dataset page: https://huggingface.co/datasets/yaak-ai/L2D.

Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. Dataset Details Dataset Description Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed] Dataset Sources [optional] Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/bluuebunny/arxiv_metadata_by_year.

Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint]. Loading dataset to load the dataset without automatically downloading and process the images (Please run the following codes with datasets==2.18.0)… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.

NU-TONIC raw SFT Full Satellite imagery and aligned land-cover outputs packaged as image–text rows for fine-tuning in SFT format. JSONL user prompts name the modality (satellite imagery vs. overhead context) where it matters. Provenance Locations: GeoGuessr-style POIs (source: stochastic/random_streetview_images_pano_v0.0.2) Optical: Sentinel-2 multispectral optical COGs from a public STAC catalog, blue/green/red or visual preview, percentile-stretched to uint8. Labels:… See the full description on the dataset page: https://huggingface.co/datasets/PEBE691445/sat-image-boundingbox-sft-full.

Dataset Card for "trivia_qa" Dataset Summary TriviaqQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaqQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. Supported Tasks and Leaderboards More Information Needed Languages… See the full description on the dataset page: https://huggingface.co/datasets/mandarjoshi/trivia_qa.

MedThinkVQA MedThinkVQA is an expert-annotated benchmark for multi-image diagnostic reasoning in radiology. Unlike prior medical VQA benchmarks that typically contain at most one image per case, MedThinkVQA requires models to extract evidence from each image, integrate cross-view information, and perform differential-diagnosis reasoning. Links GitHub: https://github.com/benluwang/MedThinkVQA Leaderboard: https://benluwang.github.io/MedThinkVQA/ Submission Guide:… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/MedThinkVQA.

MacroLens A benchmarking corpus for contextual financial reasoning under macroeconomic scenarios across 4,416 U.S. small- and micro-cap equities (2021-01-04 — 2026-03-31). MacroLens unifies seven tasks over a single point-in-time panel: contextual time-series forecasting, public valuation, financial-statement generation, scenario-conditioned return forecasting, private-company valuation, generator evaluation from natural-language descriptions, and real-estate valuation. Task… See the full description on the dataset page: https://huggingface.co/datasets/DeepAuto-AI/MacroLens.

This is a partial copy of CoVoST2 dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the EN_XX subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the validation and test subsets are available. From the XX_EN subsets, only fr, es, and zh-CN are included.

Introduction TL;DR: DreamDojo is a generalist robot world model pretrained on 44k hours of human egocentric data, showing unprecedented generalization to diverse objects and environments. Project page: https://dreamdojo-world.github.io/ Paper: https://arxiv.org/abs/2602.06949 Code: https://github.com/NVIDIA/DreamDojo How to Use Check out https://github.com/NVIDIA/DreamDojo Citation @article{gao2026dreamdojo, title={DreamDojo: A Generalist Robot… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1.