Datasets:

cfahlgren1
/

hub-stats

_id large_stringlengths 24 24	id large_stringlengths 4 123	author large_stringlengths 2 42	cardData large_stringlengths 2 1.09M ⌀	disabled bool 1 class	gated large_stringclasses 3 values	lastModified timestamp[us]date 2021-02-05 16:03:35 2026-05-15 13:15:28	likes int64 0 9.69k	trendingScore float64 0 80	private bool 1 class	sha large_stringlengths 40 40	description large_stringlengths 0 6.67k ⌀	downloads int64 0 2.94M	downloadsAllTime int64 0 143M	mainSize float64 0 306,846B ⌀	tags listlengths 1 7.92k	createdAt timestamp[us]date 2022-03-02 23:29:22 2026-05-15 13:14:54	paperswithcode_id large_stringclasses 706 values	citation large_stringlengths 0 10.7k ⌀
69e7409e244b695efe87097a	PsiBotAI/SynData	PsiBotAI	{"language": ["en"], "configs": [{"config_name": "all_clips", "data_files": [{"split": "train", "path": "viewer/clips.parquet"}]}]}	false	False	2026-05-14T02:00:47	125	80	false	47e9fb918b551c0df24bca04b337a79b7a554aa9	SynData 中文说明 Demo If the video cannot be displayed in your environment, open it directly: assets/syndata-demo.mp4 1. Overview SynData is a next-generation large-scale real-world multimodal dataset newly released by PsiBot. It comprehensively covers key dimensions including vision,...	18,457	18,501	29,260,313,664,192	[ "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-04-21T09:17:18	null	null
6a01fb055a8a01e921f585a9	TuringEnterprises/Open-MM-RL	TuringEnterprises	{"license": "mit", "language": ["en"], "pretty_name": "open-mm-rl", "size_categories": ["n<1K"], "tags": ["chemistry", "physics", "math", "biology", "science", "RL"], "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_...	false	False	2026-05-13T07:32:18	97	79	false	eceb05ae7ffe378f3a02884d93ab95a405f6db19	Dataset Summary Open-MM-RL is a multimodal STEM reasoning dataset covering Physics, Mathematics, Biology, and Chemistry. It is designed for problems that require models to interpret visual information and combine it with step-by-step analytical reasoning. Explore the full Open-MM-RL dataset (3,000 tasks comi...	3,849	3,849	31,062,538	[ "task_categories:question-answering", "language:en", "license:mit", "size_categories:n<1K", "format:parquet", "format:optimized-parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "chemistry", "physics",...	2026-05-11T15:51:33	null	null
69da71d4cf8e40febe35f7b7	ADSKAILab/Zero-To-CAD-1m	ADSKAILab	{"license": "apache-2.0", "task_categories": ["text-to-3d", "image-to-3d"], "tags": ["CAD", "CadQuery", "synthetic-data", "construction-sequence", "parametric-CAD", "3D-generation", "agentic-AI", "code-generation"], "pretty_name": "Zero-to-CAD 1M", "size_categories": ["1M<n<10M"], "language": ["en", "code"], "configs":...	false	False	2026-05-03T14:11:21	108	66	false	09dbd1805a5e73a2757f380b93042b8089cd4f3f	Zero-to-CAD 1M One million executable, interpretable CAD construction sequences synthesized entirely without real-world data. Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan, Pradeep Kuma...	21,531	21,585	349,104,973,477	[ "task_categories:text-to-3d", "task_categories:image-to-3d", "language:en", "language:code", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2604.24...	2026-04-11T16:07:48	null	null
69f434edee1d16ec78d229ce	angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k	angrygiraffe	{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["sft", "chain-of-thought", "coding", "math", "roleplay", "science", "humanities", "art", "multi-turn", "text", "json"], "pretty_name": "Claude Opus 4.6/4.7 Reasoning Dataset", "size_categories": ["1K<n<1...	false	False	2026-05-01T17:11:41	97	59	false	f0330e0ca46469b3928adef18c2b55f9476d6bd3	Background Ended up with some tokens to burn on a Claude Max plan. Assembly began during 4.6 and moved to 4.7. Model is tagged. The development evolved as it went along. The dataset has not been manually reviewed. It's entirely Claude developed. Clarification on Reasoning The reasoning is not Clau...	2,165	2,165	260,301,481	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "sft", "chain-of-thought", "coding", "math",...	2026-05-01T05:06:53	null	null
69ef6131ceb075c32613a27a	open-thoughts/AgentTrove	open-thoughts	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["agent", "code", "agentic-traces", "reinforcement-learning", "terminus-2", "harbor", "agent-traces"], "size_categories": ["1M<n<10M"]}	false	False	2026-05-07T14:20:40	134	46	false	b395a4307a2bc9950a90dc899438f149e115fc60	AgentTrove AgentTrove is the largest open-source collection of agentic interaction traces to date, released by the OpenThoughts-Agent team. It contains 1,696,847 rows drawn from 219 source datasets spanning code repair, shell scripting, mathematical problem-solving, competitive programming, and general compu...	9,564	9,564	19,552,366,847	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "agent", "code", "agentic-traces", "reinforcement-learning", ...	2026-04-27T13:14:25	null	null
69e08d8954823215aef2af15	AlienKevin/SWE-ZERO-12M-trajectories	AlienKevin	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["swe-zero", "code", "agentic", "pre-training"], "size_categories": ["10M<n<100M"]}	false	False	2026-05-14T23:54:23	46	45	false	44e028077c55e7255c328516c8bd76080fbb3840	SWE-ZERO 12M Trajectories The largest agentic-coding trace dataset to date: 112 B tokens of execution-free agentic trajectories covering 122 K pull requests, 3 K repositories, and 16 programming languages. Motivation Agentic mid-training has become a standard ingredient for frontier coding models:...	4,642	4,642	35,972,855,768	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "swe-zero", "code", "agentic", "pre-training" ]	2026-04-16T07:19:37	null	null
69e695a5d20baec02ee3039c	nvidia/Nemotron-Personas-Korea	nvidia	{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["ko"], "tags": ["synthetic", "personas", "NVIDIA", "Korean", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "s...	false	False	2026-04-23T07:42:48	450	23	false	d0a9272116a2ebf139b964ca72b8b8f604616689	Nemotron-Personas-Korea 우리나라 실제 분포에 기반한 합성 페르소나를 위한 복합 AI 시스템 A compound AI approach to personas grounded in real-world distributions 데이터셋 개요 (Overview) Nemotron-Personas-Korea는 대한민국의 실제 인구통계학적·지리적·성격 특성 분포를 기반으로 합성된 오픈소스 페르소나 데이터셋(CC BY 4.0)으로, 우리나라 인구의 다양성과 특성을 폭넓게 반영하도록 설계되었...	80,116	80,116	1,984,405,985	[ "task_categories:text-generation", "language:ko", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "format:optimized-parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "library:datadesigner", "region:u...	2026-04-20T21:07:49	null	null
69ca9b695a4dac480491fd13	lambda/hermes-agent-reasoning-traces	lambda	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["tool-calling", "function-calling", "agent", "hermes", "reasoning", "sharegpt", "sft", "traces"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "kimi", "data_files": [{"split": "train", "path": "data/kimi/tra...	false	False	2026-04-17T10:06:39	314	21	false	b92885e4f0161d4b2536512710e004d4892cac6e	Hermes Agent Reasoning Traces Multi-turn tool-calling trajectories for training AI agents using the Hermes Agent harness. Each sample is a real agent conversation with step-by-step reasoning (<think> blocks) and actual tool execution results. This dataset has two configs, one per source model: Config M...	8,265	10,337	1,616,105,008	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "format:optimized-parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "tool-calling", "function-calling...	2026-03-30T15:48:57	null	null
698f31c08d725e29501c0e3a	Qwen/WebWorldData	Qwen	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["WebWorld", "world-model", "web-agent", "browser-simulation", "a11y", "html", "xml", "markdown", "trajectories", "agent-training", "synthetic-data"], "pretty_name": "WebWorldData", "size_categories": ["1M<n<10M"]}	false	False	2026-05-08T12:12:49	23	19	false	e108c5f8e35445c9ddff71cde2a5b1fc4db4020c	WebWorldData 🌐 Overview WebWorldData is a large-scale dataset of 1.06M web interaction trajectories collected from the open web, designed for training browser world models. It is the training data behind the WebWorld model series. Each trajectory consists of sequences of (sta...	504	509	52,243,357,762	[ "task_categories:text-generation", "language:en", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "arxiv:2602.14721", "region:us", "WebWorld", "world-model", ...	2026-02-13T14:14:24	null	null
69eae63acc97dccc4e14bfe5	5551z/VisCoR-55K	5551z	{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 54844}], "download_size": 0, "dataset_size": 0}}	false	False	2026-04-30T10:51:23	23	19	false	98b8087267ba987bd9c2110b9d51f72f716a6430	VisCoR-55K Dataset VisCoR-55K is a high-quality dataset for visual reasoning, spanning five categories: General, Reasoning, Math, Graph/Chart, and OCR. This release contains three components: VQA Samples: Original visual question-answer pairs. Contrastive Counterparts: Matched contrastive VQA pairs construc...	185	185	8,143,797,508	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2603.02556", "region:us" ]	2026-04-24T03:40:42	null	null
6918abcd7b63899ef32fd37d	Modotte/CodeX-2M-Thinking	Modotte	{"license": "apache-2.0", "pretty_name": "CodeX-5M-Thinking", "dataset_name": "Modotte/CodeX-5M-Thinking", "size_categories": ["1M<n<10M"], "language": ["en"], "task_categories": ["text-generation", "question-answering"], "tags": ["Coding", "Code", "CodeX", "Modotte", "LLM-training", "synthetic", "curated", "benchmark"...	false	False	2026-02-10T07:23:38	83	13	false	f9a4622fe9ccaa71509beea80e3bc69739cbbfa2	Modotte Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning. This dataset is fully synthetic, curated from high-quality public sources and enhanced...	5,614	14,442	24,444,876,787	[ "task_categories:text-generation", "task_categories:question-answering", "annotations_creators:machine-generated", "annotations_creators:expert-verified", "multilinguality:monolingual", "source_datasets:Modotte internal synthetic generation", "language:en", "license:apache-2.0", "size_categories:1M<...	2025-11-15T16:35:25	null	null
69fed0efdacd5a79d81aba6b	TeichAI/DeepSeek-v4-Pro-Agent	TeichAI	{"pretty_name": "DeepSeek v4 Pro Agent Traces", "tags": ["agent-traces", "pi", "distillation", "deepseek/deepseek-v4-pro", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]}	false	False	2026-05-12T04:27:13	13	13	false	debc871d277b6ecf5cc976e40b476ff81fcf8ffe	This dataset was generated using teich by TeichAI Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below. DeepSeek v4 Pro Agent Traces This directory contains raw agent trace files generated by teich. All assistant responses were generated by dee...	1,276	1,276	279,544,871	[ "size_categories:1K<n<10K", "format:json", "format:agent-traces", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "agent-traces", "pi", "distillation", "deepseek/deepseek-v4-pro", "teich" ]	2026-05-09T06:15:11	null	null
69f21428d795645a9d51b6cb	5551z/VisCoR_Contrast	5551z	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "pairdata_*.parquet"}]}], "dataset_info": {"features": [{"name": "anchor_image", "dtype": "image"}, {"name": "anchor_question", "dtype": "string"}, {"name": "anchor_answer", "dtype": "string"}, {"name": "counterpart_image", "dtype": "imag...	false	False	2026-04-30T01:37:13	15	12	false	88e338316e253020ebf7929e319335bfb29d043b	VisCoR-55K Contrastive Pairs This dataset contains contrastive visual question-answering (VQA) pairs for VisCoR-55K, a high-quality visual reasoning dataset spanning five categories: General, Reasoning, Math, Graph/Chart, and OCR. This release contains three components: VQA Samples: Original visual question...	276	276	16,351,640,998	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2603.02556", "region:us" ]	2026-04-29T14:22:32	null	null
69fc9ace5e0d5b0eb7a969e5	Exgentic/agent-llm-traces	Exgentic	{"license": "cdla-permissive-2.0", "format": "agent-traces", "task_categories": ["text-generation"], "language": ["en"], "tags": ["llm", "traces", "opentelemetry", "benchmarks", "agents"]}	false	False	2026-05-14T18:50:50	12	12	false	6bbbbb0b3790ba42e5d86f854e7d00a2a263878e	Multi-Benchmark LLM Agent Traces A comprehensive dataset of OpenTelemetry traces capturing LLM inference behavior across multiple agent frameworks, benchmarks, and model providers. This dataset enables research into LLM performance analysis, agent behavior patterns, and inference optimization. Collected by E...	344	344	983,601,206	[ "task_categories:text-generation", "language:en", "license:cdla-permissive-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "llm", "traces", "opentelemetry", ...	2026-05-07T13:59:42	null	null
621ffdd236468d709f181e5e	cais/mmlu	cais	{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswit...	false	False	2024-03-08T20:36:26	733	11	false	c30699e8356da336a370243923dbaf21066bb9fe	Dataset Card for MMLU Dataset Summary Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branc...	520,668	41,342,268	270,035,224	[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text"...	2022-03-02T23:29:22	mmlu	null
69ea840a9a3a30e09b700a00	ShadenA/MathNet	ShadenA	{"pretty_name": "MathNet v0 \u2014 Olympiad Math Reasoning & Retrieval", "license": "cc-by-4.0", "repository": "https://github.com/ShadeAlsha/MathNet", "contact_email": "shaden@mit.edu", "homepage": "https://mathnet.mit.edu", "task_categories": ["question-answering", "text-generation", "image-to-text"], "language": ["e...	false	False	2026-04-27T23:48:47	64	11	false	ae12e35eef0fc52bbbef270d6ef0f5b002252eb9	Quick Start · Overview · Tasks · Comparison · Dataset Stats · Data Sources · Pipeline · Schema · License · Citation This is the official MathNet v0. A larger version v1 will be uploaded soon (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1. Qu...	21,488	21,495	738,145,122	[ "task_categories:question-answering", "task_categories:text-generation", "task_categories:image-to-text", "language:en", "language:pt", "language:es", "language:fr", "language:it", "language:sr", "language:sl", "language:de", "language:zh", "language:ro", "language:ko", "language:nl", ...	2026-04-23T20:41:46	null	null
69fc7f0d917d11cfe2e23841	junwatu/indonesian-recipes	junwatu	{"license": "other", "license_name": "research-use-as-is", "language": ["id"], "task_categories": ["text-generation"], "tags": ["recipes", "indonesian", "cooking", "dishes", "food"], "size_categories": ["10K<n<100K"], "pretty_name": "Indonesian Recipes", "configs": [{"config_name": "default", "data_files": [{"split": "...	false	auto	2026-05-09T06:36:26	15	11	false	00b37b76ee548ee8ca0acc3b238a230c8878ebd3	Indonesian Recipes A structured collection of Indonesian recipes for fine-tuning text-generation models. Each row is a single recipe with a title, an ingredient list, and ordered preparation steps. Schema Column Type Description title string Recipe name ingredients list<string> One it...	161	161	20,581,139	[ "task_categories:text-generation", "language:id", "license:other", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "recipes", "indonesian", "cooking", "dishe...	2026-05-07T12:01:17	null	null
69fd2ace3d600fa5f6587a10	blanchon/opencs2_dataset	blanchon	{"license": "cc-by-4.0", "task_categories": ["video-classification", "reinforcement-learning", "other"], "language": ["en"], "tags": ["opencs2", "counter-strike-2", "torchcodec", "video", "audio", "parquet"], "pretty_name": "OpenCS2 - POV Renders", "configs": [{"config_name": "pov_rounds", "data_files": [{"split": "tra...	false	False	2026-05-04T15:38:59	11	11	false	3934b59905159337b01eb174e33ce772f14506ad	OpenCS2 - POV Renders Browse with the OpenCS2 Viewer - every match, map and round, with all 10 player POVs synced on one timeline. Tick-aligned Counter-Strike 2 POV training clips, rendered from blanchon/cs2_dataset_demo. Each row in the main table is one player's perspective for one round; ten POVs per r...	20,182	20,182	10,628,527,328,690	[ "task_categories:video-classification", "task_categories:reinforcement-learning", "task_categories:other", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "modality:video", "modality:audio", "library:datasets", "library:...	2026-05-08T00:14:06	null	null
66212f29fb07c3e05ad0432e	HuggingFaceFW/fineweb	HuggingFaceFW	{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data//"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*...	false	False	2025-07-11T20:16:53	2,793	10	false	9bb295ddab0e05d785b879661af7260fed5140fc	🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performa...	922,781	8,019,186	54,812,538,723,397	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:10B<n<100B", "modality:tabular", "modality:text", "arxiv:2306.01116", "arxiv:2109.07445", "arxiv:2406.17557", "doi:10.57967/hf/2493", "region:us" ]	2024-04-18T14:33:13	null	null
69da0cb18a57dde89bb6f3a8	llm-jp/Jagle	llm-jp	{"language": ["ja"], "size_categories": ["1M<n<10M"], "license": "other"}	false	False	2026-05-12T00:53:55	10	10	false	39a6213d6f0b1814c6b4ace4de3e37db00c404a0	Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision–Language Models \| 🤗 HuggingFace \| 📄 Paper \| 🧑‍💻 Code \| Overview Jagle is a large-scale Japanese multimodal post-training dataset, comprising approximately 9.2 million instances across divers...	536	639	4,292,475	[ "language:ja", "license:other", "size_categories:1M<n<10M", "arxiv:2604.02048", "region:us" ]	2026-04-11T08:56:17	null	null
69e1bed4cc8fb2e676e4aa7c	Jackrong/GLM-5.1-Reasoning-1M-Cleaned	Jackrong	{"license": "apache-2.0", "language": ["en", "zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft", "distillation", "glm", "glm-5.1", "cleaned"], "configs": [{"config_name": "main", "default": true, "d...	false	False	2026-04-19T05:05:17	194	10	false	f6d6ccafe40359d5ec2515ee25e92aac8cae9c3d	GLM-5.1-Reasoning-1M-Cleaned GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversatio...	10,821	10,821	31,734,914,777	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "language:zh", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning",...	2026-04-17T05:02:12	null	null
69f75f0cebff1de2d69553be	r0b0tlab/deepseek-hermes-reasoning-traces	r0b0tlab	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["hermes", "agent", "tool-calling", "reasoning", "sft", "lora", "function-calling", "deepseek", "chatml"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "tr...	false	False	2026-05-04T00:18:27	27	10	false	28a1a874f9db2b8d41907a13f1c246ab6bba94f8	DeepSeek V4 Pro Hermes Reasoning Traces 19,331 multi-turn ChatML + Hermes reasoning traces generated by DeepSeek V4 Pro. Designed for LoRA fine-tuning local models to operate as Hermes Agent instances. Quick Start \ Splits Split Traces train 16,431 valid 1,933 test 967 ...	1,527	1,527	77,262,957	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "format:optimized-parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "hermes", "...	2026-05-03T14:43:24	null	null
625552d2b339bb03abe3432d	openai/gsm8k	openai	{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_na...	false	False	2026-03-23T10:18:13	1,306	9	false	740312add88f781978c0658806c59bc2815b9866	Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These p...	931,476	11,478,812	5,900,352	[ "benchmark:official", "benchmark:eval-yaml", "task_categories:text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modal...	2022-04-12T10:22:10	gsm8k	null
6655eb19d17e141dcb546ed5	HuggingFaceFW/fineweb-edu	HuggingFaceFW	{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data//"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"},...	false	False	2025-07-11T20:16:53	1,072	9	false	87f09149ef4734204d70ed1d046ddc9ca3f2b8f9	📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb data...	571,841	7,089,153	5,835,742,481,176	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2406.17557", "arxiv:2404.14219", "arxiv:2401.10020", ...	2024-05-28T14:32:57	null	null
69b186f91cde8c71bb8f76b0	Roman1111111/claude-opus-4.6-10000x	Roman1111111	{"license": "mit"}	false	False	2026-04-05T13:42:24	359	9	false	d6fe6aafcf5db8141153a0828c791eeee512b171	This is a high-fidelity reasoning dataset synthesized using Claude Opus 4.6. The dataset is designed to capture the model's internal "Chain of Thought" and reasoning traces, specifically focusing on mathematical accuracy and structured logical deduction. The dataset is intended for Supervised Fine-Tuning (SFT) and Dist...	7,135	12,607	13,409,472	[ "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-03-11T15:15:05	null	null
69e17e7dcdbd37ff8333732b	nvidia/SWE-Hero-openhands-trajectories	nvidia	{"dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "trajectory_id", "dtype": "string"}, {"name": "trajectory", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "too...	false	False	2026-05-08T17:10:16	12	9	false	150bc119e52c647216fce285fd801f16b6fd745b	SWE-Hero Trajectories: Execution-based Fine-tuning for Software Engineering Agents Data Overview SWE-Hero Trajectories is an agentic instruction tuning dataset designed to advance the capabilities of LLMs in software engineering. This dataset comprises 34k agent trajectories collected using the O...	792	792	2,402,031,292	[ "license:cc-by-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2604.01496", "region:us", "code", "synthetic", "tools", "agents", "software" ]	2026-04-17T00:27:41	null	null
69eb18f2b34c8304df385f54	Jackrong/DeepSeek-V4-Distill-8000x	Jackrong	{"license": "mit", "language": ["en"], "pretty_name": "DeepSeek-V4-Distill-8100x", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["reasoning", "distillation", "supervised-fine-tuning", "chain-of-thought", "deepseek-v4-flash"], "source_datasets": ["Jackrong/GLM-5.1-Reasoning-1M-Cleaned...	false	False	2026-04-24T08:32:56	78	9	false	25f6ba88065a5add3c34a36b2eb43f55ff709b6f	🐳 DeepSeek-V4-Distill-8100x Dataset Summary DeepSeek-V4-Distill-8100x is a supervised fine-tuning dataset for reasoning-oriented distillation. The question prompts come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, and the answers were generated by the teacher model DeepSeek-V4-Flas...	9,985	9,985	142,164,063	[ "task_categories:text-generation", "source_datasets:Jackrong/GLM-5.1-Reasoning-1M-Cleaned", "language:en", "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning", "di...	2026-04-24T07:17:06	null	null
6a02c56f61fcafc28add25ba	alibaba-multimodal-industrial-ai/IndustryBench	alibaba-multimodal-industrial-ai	{"language": ["zh", "en", "ru", "vi"], "license": "mit", "task_categories": ["question-answering", "text-generation"], "pretty_name": "IndustryBench", "size_categories": ["1K<n<10K"]}	false	False	2026-05-13T05:23:50	10	9	false	11ef6081abb6699f29d7eacb24829846fc879cfd	IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs 💻Github \| 📝Paper IndustryBench is a multi-lingual benchmark for evaluating the industrial domain knowledge of large language models. It comprises 2,049 expert-curated QA pairs spanning 12 industrial sectors, with human-reviewed translations...	69	69	16,213,098	[ "task_categories:question-answering", "task_categories:text-generation", "language:zh", "language:en", "language:ru", "language:vi", "license:mit", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "a...	2026-05-12T06:15:11	null	null
68e3ebe623e838a4741abb06	AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1	AlicanKiraz0	{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["cybersecurity", "defensive-security", "instruction-tuning"], "size_categories": ["10K<n<100K"], "dataset_info": {"version": "1.1.0"}}	false	False	2026-04-22T10:29:32	86	8	false	fd7967ddda760281a2f01f4367f7b78bd128f3ec	Cybersecurity Defense Instruction-Tuning Dataset (v2.1) Created by Alican Kiraz TL;DR A ready-to-train dataset of 99,870 high-quality system / user / assistant triples for defensive, alignment-safe cybersecurity SFT training. Apache-2.0 licensed and production-ready. Scope: OWASP Top 10, MITRE A...	9,168	14,691	433,544,195	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "cybersecurity", "defensive-security", "instruction-tuning" ]	2025-10-06T16:18:46	null	null
69c2007e19d6a09b73f58d79	mvp-lab/LLaVA-OneVision-2-Data	mvp-lab	{"license": "apache-2.0", "task_categories": ["video-text-to-text", "visual-question-answering", "image-text-to-text"], "language": ["en"], "tags": ["llava", "multimodal", "video", "spatial-reasoning"], "size_categories": ["10M<n<100M"], "configs": [{"config_name": "viewer_caption_30s", "data_files": [{"split": "previe...	false	False	2026-05-11T14:43:43	12	8	false	e73747a5aff28d10c6207841f95e290b5467ca07	LLaVA-OneVision-2-Data Training data for the LLaVA-OneVision-2 multimodal model family, covering large-scale video and spatial reasoning corpora used in mid-training. Dataset Composition Subset Format Description mid_training_video/60s_rest/ WebDataset (.tar) 10,809 shards of ~60s video...	96,810	99,209	66,575,112,910,817	[ "task_categories:video-text-to-text", "task_categories:visual-question-answering", "task_categories:image-text-to-text", "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "format:optimized-parquet", "modality:image", "modality:text", "modality:video", "library:data...	2026-03-24T03:09:50	null	null
69e1158df72d876b2c10188a	nvidia/Nemotron-Image-Training-v3	nvidia	{"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "image-text-to-text"], "pretty_name": "Nemotron Image Training v3", "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "messages", "sequence": {"struct": [{"name": "role", "dtype": "string"}...	false	False	2026-04-28T08:35:01	63	8	false	7656391d4d4cb11ec3722b34f10d499435de0460	Nemotron Image Training v3 Versions Date Commit Changes 2026-04-28 HEAD Initial commit. Dataset Description Nemotron Image Training v3 is a collection of image-centric multimodal training data for vision–language models. Similar to Nemotron-VLM-Dataset v2, it was curated...	6,774	6,774	465,130,164,351	[ "task_categories:visual-question-answering", "task_categories:image-text-to-text", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-04-16T16:59:57	null	null
69f745f69d05f316b2c67a54	MolDeTox/MolDeTox	MolDeTox	{"license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation"], "tags": ["molecule", "toxicity", "drug-discovery", "benchmark", "llm", "vlm"], "pretty_name": "MolDeTox", "configs": [{"config_name": "task1_single", "data_files": [{"split": "train", "path": "MolDeTox_QA/train/train_task1_single.jso...	false	False	2026-05-05T05:45:27	10	8	false	1ed43c5d3553f06f561ed5d5b1b88eebb7881593	MolDeTox Dataset Overview MolDeTox is a benchmark dataset designed to evaluate toxicity-aware molecular editing capabilities of LLMs and VLMs. The dataset is constructed based on the concept of toxicity cliffs, where structurally similar molecules exhibit opposite toxicity labels. This design enab...	142	142	629,248,174	[ "task_categories:question-answering", "task_categories:text-generation", "license:cc-by-4.0", "size_categories:100K<n<1M", "modality:tabular", "modality:text", "region:us", "molecule", "toxicity", "drug-discovery", "benchmark", "llm", "vlm" ]	2026-05-03T12:56:22	null	null
69fbb6f7668a521b200b0ec6	sequelbox/Tachibana4-DeepSeek-V4-Pro	sequelbox	{"license": "apache-2.0", "tags": ["tachibana", "tachibana-4", "agentic", "agentic-coding", "python", "c++", "c#", "c", "rust", "java", "javascript", "typescript", "go", "haskell", "shell", "r", "ruby", "algorithms", "data-structures", "concurrency", "api", "sql", "database", "auth", "ui", "mobile", "gamedev", "physics...	false	False	2026-05-07T01:09:32	15	8	false	80304ea7aaa9dff66d3b674702d9534da7bdc7fe	Click here to support our open-source dataset and model releases - help us speed up our release schedule! Tachibana 4 is an agentic coding dataset, testing the limits of DeepSeek-V4-Pro's coding skills: Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topi...	300	300	910,052,982	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "doi:10.57967/hf/8696", "region:us", "tachibana", "tachibana-4", "agentic", ...	2026-05-06T21:47:35	null	null
69fe9efac30a31098aa77b41	infly/Infinity-Doc2-5M	infly	{"license": "mit", "language": ["en", "zh"], "pretty_name": "Infinity-Doc2-5M", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "demo_data/*"}]}], "dataset_info": {"features": [{"name": "images", "sequence": "image"}, {"name": "conversations", "list": [{...	false	False	2026-05-15T09:56:56	8	8	false	3b8b6f4f0ace491877557a18ca93161eea0c181e	Infinity-Doc2-5M 💻 Github \| 🤗 Infinity-Parser2-Pro \| 🤗 Infinity-Parser2-Flash \| 📄 Paper(coming soon...) \| 🚀 Demo Infinity-Doc2-5M is a training dataset for document parsing scenarios, with the following characteristics: Diverse document types: This dataset contains 5 million samples coveri...	287	287	77,365,843,524	[ "language:en", "language:zh", "license:mit", "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-05-09T02:42:02	null	null

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Changelog

NEW Changes March 11th 2026

Added new split: arxiv_papers, sourced from the Hugging Face /api/papers endpoint
papers continues to point to daily_papers.parquet, which is the Daily Papers feed

NEW Changes July 25th

added baseModels field to models which shows the models that the user tagged as base models for that model

Example:

{
  "models": [
    {
      "_id": "687de260234339fed21e768a",
      "id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
    }
  ],
  "relation": "quantized"
}

NEW Changes July 9th

Fixed issue with gguf column with integer overflow causing import pipeline to be broken over a few weeks ✅

NEW Changes Feb 27th

Added new fields on the models split: downloadsAllTime, safetensors, gguf
Added new field on the datasets split: downloadsAllTime
Added new split: papers which is all of the Daily Papers

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