Datasets:

cfahlgren1
/

hub-stats

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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	343	266	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...	36,722	36,722	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
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	129	69	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...	3,220	3,220	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
69b186f91cde8c71bb8f76b0	Roman1111111/claude-opus-4.6-10000x	Roman1111111	{"license": "mit"}	false	False	2026-04-05T13:42:24	308	50	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...	7,498	9,362	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
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	261	48	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...	8,217	8,217	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
69e7c30f4bccf73cfe458752	openai/healthbench-professional	openai	{"license": "mit", "tags": ["health", "healthbench"], "pretty_name": "HealthBench Professional"}	false	False	2026-04-22T16:09:30	39	37	false	349962fd46dd02343a0d8a606491baf59154ea1a	Contains the data for the HealthBench Professional eval. Each example contains: conversation: list of user / assistant messages, ending in a user message rubric_items: list of rubric items, each containing criterion_text and points use_case: one of consult, writing, or research type: one of good_faith or red_teaming d...	2,984	2,984	2,759,827	[ "license:mit", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "health", "healthbench" ]	2026-04-21T18:33:51	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	59	32	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...	3,704	8,412	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
69e4aa7ea8ad7ec14c63ae71	Roman1111111/claude-sonnet-4.6-120000x	Roman1111111	null	false	False	2026-04-19T10:59:32	56	30	false	ab722bb8ea6e47386dc4c8227246640414037fe5	license: mit task_categories: text-generation text2text-generation language: en tags: reasoning uncensored math code claude-sonnet-4.6 claude-opus-4.6 gemini-3.1-pro size_categories: 100K<n<1M Please support if possible claude-sonnet-4.6-natural-large Sonnet4.6 NATURAL REASONING Multi-Domain(covered all p...	3,096	3,096	800,920,542	[ "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-04-19T10:12:14	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	30	29	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...	1,220	1,220	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
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	31	28	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...	9,286	9,288	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
69d3b00b2d56eb23d8824420	badlogicgames/pi-mono	badlogicgames	{"pretty_name": "coding agent session traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "coding-agent", "pi-share-hf"], "language": ["en", "code"], "license": "other"}	false	False	2026-04-06T13:10:36	101	22	false	dac2a1d3ba12dda597b973a791a77618ccb5f413	Coding agent session traces for badlogicgames/pi-mono This dataset contains redacted coding agent session traces collected while working on https://github.com/badlogic/pi-mono.git. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed deterministic...	19,627	19,627	224,783,955	[ "task_categories:text-generation", "language:en", "language:code", "license:other", "size_categories:n<1K", "format:json", "format:agent-traces", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "agent-traces", "coding-agent", "...	2026-04-06T13:07:23	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	49	19	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...	2,460	10,510	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
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	19	19	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...	0	0	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", "region:us" ]	2026-04-16T16:59:57	null	null
69e2cade98b9dc3568831558	lordx64/reasoning-distill-claude-opus-4-7-max	lordx64	{"license": "apache-2.0", "language": ["en"], "tags": ["reasoning", "chain-of-thought", "distillation", "claude", "opus-4-7", "synthetic"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "source_dataset", "dtype": "string"}, {"name": "source_idx", "dtype"...	false	False	2026-04-20T22:38:17	27	19	false	1fcae97d571e7ddad77139e82f79e991167b14e5	Reasoning traces from Claude Opus 4.7 — raw 8,124 reasoning conversations produced by Anthropic Claude Opus 4.7 with extended-thinking enabled, for distillation into open-source language models. Each row contains the full API response (thinking + final answer) for a single prompt. Provenance — import...	512	512	19,210,087	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "format:optimized-parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning", "chain-of-thought", ...	2026-04-18T00:05:50	null	null
69e36cc5bcc2181a635990b4	ZhihaoNan/AtomBlock-WebUI	ZhihaoNan	{"license": "cc-by-nc-sa-4.0", "task_categories": ["object-detection"], "language": ["en"], "tags": ["agent", "ui", "web", "yolo"], "pretty_name": "AtomBlock-WebUI", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "parquet/*.parquet"}]}]}	false	False	2026-04-24T04:53:30	42	18	false	262927bcd03903c27b804efe38447f1ad24d2007	AtomBlock-WebUI A Synthetic Web UI Dataset Featuring Pixel-Perfect Atomic Elements and Structural Blocks, generated via LLM-augmented HTML rendering and headless browser screenshot capture. Overview AtomBlock-WebUI contains ~9,700 full-page web screenshots with YOLO-format bounding box annotations...	1,980	1,980	63,330,099,043	[ "task_categories:object-detection", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:1K<n<10K", "format:parquet", "format:optimized-parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "region:us", "agent", "u...	2026-04-18T11:36:37	null	null
69eb8e1aab827af06186f972	SALT-NLP/SWE-chat	SALT-NLP	{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "agent", "traces", "human-ai-collaboration", "agent-traces", "coding-agent", "coding-sessions"], "pretty_name": "SWE-chat", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "conversations", "data_files": [{"sp...	false	auto	2026-04-29T08:20:21	18	18	false	0912b15ee55f29f1295be9277bc207bbe360c84e	SWE-chat: Coding Agent Interactions From Real Users in the Wild 📄 Paper: arxiv.org/abs/2604.20779 🌐 Website: swe-chat.com Dataset Summary SWE-chat captures real-world AI coding sessions from developers using AI coding assistants (Claude Code, Codex, Gemini CLI, and others via the Entire.io CLI...	112	112	12,786,344,292	[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "arxiv:2604.20779", "region:us", "code", "agent", "trace...	2026-04-24T15:36:58	null	null
69e59f7aa21023d609bc43bb	tencent/MegaStyle-1.4M	tencent	{"license": "other", "task_categories": ["text-to-image"], "tags": ["style transfer", "text-to-image generation"], "language": ["en"], "size_categories": ["1M<n<10M"]}	false	False	2026-04-20T09:03:50	35	16	false	5625ac67efa1210e19bf138c0644b16aeaed252a	Dataset of MegaStyle. MegaStyle-1.4M is a large-scale style dataset built through a scalable pipeline that leverages consistent text-to-image style mapping of Qwen-Image. It combines 170K curated style prompts with 400K content prompts to generate 1.4M high-quality images that share strong intra-style consistency while...	872	872	44,952,941,148	[ "task_categories:text-to-image", "language:en", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2604.08364", "region:us", "style transfer", "text-to-image...	2026-04-20T03:37:30	null	null
681139b8ff0764f384f0b38e	SWE-bench/SWE-bench_Verified	SWE-bench	{"dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "...	false	False	2026-02-27T20:36:38	48	14	false	91aa3ed51b709be6457e12d00300a6a596d4c6a3	Dataset Summary SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process. The dataset collects 500 test I...	102,028	910,848	2,096,790	[ "benchmark:official", "benchmark:eval-yaml", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2025-04-29T20:42:32	null	null
69e2d226bf20d3a18fad97af	lordx64/reasoning-distill-opus-4-7-max-sft	lordx64	{"license": "apache-2.0", "language": ["en"], "tags": ["reasoning", "chain-of-thought", "distillation", "claude", "opus-4-7", "sft", "qwen-chat-template"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "tr...	false	False	2026-04-20T22:38:18	23	14	false	1cbdcd72a8a6681b3713c1d31f01c711b816d1a4	Reasoning traces from Claude Opus 4.7 — SFT-ready 7,823 single-turn reasoning conversations from Claude Opus 4.7 reformatted for supervised fine-tuning with trl.SFTTrainer + train_on_responses_only. Each row is a single text field containing a full Qwen-style chat-template conversation. Provenance ...	480	480	15,815,347	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "format:optimized-parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning", "chain-of-thought", ...	2026-04-18T00:36:54	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	1,282	13	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...	831,147	10,919,971	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
66048fd19fcaed55efc919c7	ai4privacy/pii-masking-300k	ai4privacy	{"license": "other", "license_name": "license.md", "language": ["en", "fr", "de", "it", "es", "nl"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-gener...	false	False	2026-04-04T16:18:22	92	13	false	259743348cf6cba118f3149a3cffe1824390946c	Purpose and Features 🌍 World's largest open dataset for privacy masking 🌎 The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, especially in the context of AI assistants and LLMs. Key facts: OpenPII-220k text entries have 27 PII classe...	4,956	51,088	803,425,836	[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-gene...	2024-03-27T21:29:53	null	null
69b3fa8c8dd0cb1205153394	TAAC2026/data_sample_1000	TAAC2026	{"license": "cc-by-nc-4.0", "tags": ["TAAC2026", "recommendation"]}	false	False	2026-04-10T09:07:28	72	13	false	28866848945708ba6a5949d0e2a3d91a61b93109	TAAC2026 Demo Dataset (1000 Samples) [!WARNING] ⚠️Update[2026.04.10]: This demo dataset has been updated to newest version with the following changes: The parquet file is now a flat column layout, with all features as top-level columns. Add a sequence feature, rename feature names and update some features....	10,570	15,671	40,274,629	[ "license:cc-by-nc-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "TAAC2026", "recommendation" ]	2026-03-13T11:52:44	null	null
69c45b9e5030946bd70055bf	ianncity/KIMI-K2.5-1000000x	ianncity	{"license": "apache-2.0", "language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "tags": ["reasoning", "chain-of-thought", "instruction-tuning", "sft"], "configs": [{"config_name": "General-Distillation", "data_files": [{"split": "train", "path": "kimi-k2.5-m...	false	False	2026-04-07T02:04:22	252	13	false	de244b70a988b37cecd56ab69052591b3f28e845	KIMI-K2.5-1000000x 1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions) Distribution: Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#) Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Scie...	5,677	5,749	19,672,279,661	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning", "chain-of-thou...	2026-03-25T22:03:10	null	null
69cf68ab0689e4caa5b6a50d	Kassadin88/Claude-Distills	Kassadin88	{"license": "mit", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["claude", "distillation", "reasoning", "instruction-tuning", "sft"], "size_categories": ["100K<n<1M"]}	false	False	2026-04-23T02:12:55	25	13	false	16ffde335dbdb3a3ba2f2e832b71e6c618865380	Claude-Distills A curated collection of open-source Claude distillation datasets, unified and deduplicated. Note: This repo only provides unified formatting, deduplication, and documentation. All credits go to the original data creators. I did NOT create any of the original data. Data Sources ...	592	592	888,591,072	[ "task_categories:text-generation", "task_categories:question-answering", "language:en", "license:mit", "size_categories:100K<n<1M", "region:us", "claude", "distillation", "reasoning", "instruction-tuning", "sft" ]	2026-04-03T07:13:47	null	null
66755d9d9f2810b0096ac389	hf-audio/open-asr-leaderboard	hf-audio	{"dataset_info": [{"config_name": "ami", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "dataset", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "audio_length_s", "dtype": "float64"}], "splits": [{"name": "test", "num_bytes":...	false	False	2026-04-15T15:21:36	23	12	false	20a009a3a37d035d965722e5feb890ba7f2d46ac	ESB Test Sets: Parquet & Sorted This dataset takes the open-asr-leaderboard/datasets-test-only data and sorts each split by audio length. The format is also changed, from custom loading script (un-safe remote code) to parquet (safe). Broadly speaking, this dataset was generated with the following code-snipp...	20,314	147,302	20,843,391,762	[ "benchmark:official", "benchmark:eval-yaml", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:polars", "library:mlcroissant", "arxiv:2510.06961", "region:us" ]	2024-06-21T11:01:49	null	null
6954cdff0a36f347a9b323fd	genrobot2025/10Kh-RealOmin-OpenData	genrobot2025	{"license": "cc-by-sa-4.0", "task_categories": ["robotics", "reinforcement-learning"], "language": ["en", "zh"], "tags": ["agent", "robotic", "real-world", "dual-arm", "video", "vla", "embodied intelligence"], "size_categories": ["n>1T"]}	false	auto	2026-04-24T05:02:26	211	12	false	fcbc0d38550e134f273426aa7c9cc2b491270bc4	Boasting over 13,000 hours of cumulative data and 5 million+ clips, it ranks as the largest open-source embodied intelligence dataset in the industry. Update Notes：Stage 3 data upload completed. 13,000+ hours of pure dual-hand data with frame-level alignment latency < 1ms Full high-precision trajectory re...	81,392	441,821	36,943,684,733,950	[ "task_categories:robotics", "task_categories:reinforcement-learning", "language:en", "language:zh", "license:cc-by-sa-4.0", "size_categories:n>1T", "modality:video", "region:us", "agent", "robotic", "real-world", "dual-arm", "video", "vla", "embodied intelligence" ]	2025-12-31T07:17:19	null	null
639244f571c51c43091df168	Anthropic/hh-rlhf	Anthropic	{"license": "mit", "tags": ["human-feedback"]}	false	False	2023-05-26T18:47:34	1,727	11	false	09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa	Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preferenc...	34,678	1,860,537	94,745,957	[ "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2204.05862", "region:us", "human-feedback" ]	2022-12-08T20:11:33	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	2,773	11	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...	658,522	7,218,206	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
68465f1ba516bd14fc146e1f	nvidia/Nemotron-Personas-USA	nvidia	{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "...	false	False	2025-12-16T19:13:23	295	11	false	5b4cd35ab46490c1da1bd2b5a2324d6f871be180	Nemotron-Personas-USA A compound AI approach to personas grounded in real-world distributions v1.1 Update The v1.1 update introduces the following changes: leverage openai/gpt-oss-120b model instead of mistralai/Mixtral-8x22B-v0.1 model to improve data quality and diversity increase the n...	11,063	120,159	2,689,226,423	[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "library:datadesigner", "region:us", "synthetic", "personas", "NVIDIA", "da...	2025-06-09T04:12:11	null	null
69e853f9dcd29543e03131b7	ART-3D/H3D_v1	ART-3D	{"license": "cc-by-4.0", "language": ["en"], "pretty_name": "H\u00b3D: High-quality Holistic 3D Editing Dataset", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-3d", "image-to-image"], "tags": ["3d-editing", "part-level", "slat", "trellis", "instruction-following"], "configs": [{"config_name": "all", "...	false	False	2026-04-24T14:31:12	12	11	false	27afd10e2384950abab18add94347ae84262b69b	H3D_v1 is a part-level instruction-based 3D editing dataset. Each record is a (before, after) pair of 3D SLAT latents + rendered 2D views, annotated with a natural-language edit prompt. Seven edit types are covered: deletion, addition, modification, scale, material, color, and global style transfer.	517	517	58,798,881,470	[ "task_categories:text-to-3d", "task_categories:image-to-image", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "region:us", "3d-editing", "part-level", "slat", "trellis", "instruction-following" ]	2026-04-22T04:52:09	null	@misc{h3d_v1_2026, title = {H3D_v1: a part-level instruction-based 3D editing dataset}, author = {ART-3D}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/ART-3D/H3D_v1} }
69ea0877818bde4ec63ce27e	NuTonic/sat-image-boundingbox-sft-full	NuTonic	{"license": "apache-2.0", "task_categories": ["image-text-to-text"], "language": ["en"], "tags": ["satellite", "land-cover", "lfm-vl", "geospatial", "sat", "earth", "observation", "land", "sft", "sentinel", "mapbox", "terra"], "pretty_name": "NU-TONIC raw SFT Full", "size_categories": ["1M<n<10M"]}	false	False	2026-04-23T12:57:03	11	11	false	2c75718766491669b96f3aae8d0aa86057ba5b5a	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_s...	1,647	1,647	124,443,338,936	[ "task_categories:image-text-to-text", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:json", "modality:image", "modality:text", "modality:geospatial", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "satellite", "land-...	2026-04-23T11:54:31	null	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	1,046	10	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...	369,568	6,681,679	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
69ada382e33c0fe7d096f38c	nvidia/Nemotron-SFT-Math-v3	nvidia	{"language": ["en"], "license": ["cc-by-4.0", "cc-by-sa-4.0"], "task_categories": ["text-generation"], "tags": ["math"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train.jsonl"}]}]}	false	False	2026-04-28T22:38:45	26	10	false	ff4439c1073c87e006ab7ee5f1e5e28c4790dab3	Dataset Description The dataset was updated on April 27th, 2026 to fix data formatting issues! Nemotron-Math-v3 is a large-scale mathematical reasoning dataset containing model-generated reasoning trajectories produced both with and without Python Tool-Integrated Reasoning (TIR). Chain-of-thought (CoT) solu...	1,189	2,467	154,135,301,849	[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "license:cc-by-sa-4.0", "arxiv:2512.15489", "region:us", "math" ]	2026-03-08T16:27:46	null	null
69eae8d5541105e37c7f0af5	beyoru/Deepseek-v4-pro-max-distill-1000x	beyoru	{"license": "apache-2.0", "language": ["en"], "task_categories": ["text-generation"], "tags": ["reasoning", "distillation", "chain-of-thought", "deepseek", "synthetic", "deepseek-v4-pro"], "size_categories": ["n<1K"]}	false	False	2026-04-29T07:12:35	10	10	false	73c6050253eb8533a34afcea19497799029ba9a7	Overeview This dataset contains reasoning traces and final answers generated by DeepSeek-V4-Pro (reasoning_effort=max, thinking.enabled=true) using prompts sampled from Jackrong/GLM-5.1-Reasoning-1M-Cleaned. Goal: just check quality Update: The dataset have fully 1000 samples in 04/27/2026 cost only ~$5.46...	174	174	27,774,328	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us", "reasoning", "distillation", "chain-of-thought", "deepseek", "...	2026-04-24T03:51:49	null	null
65d79d224f7ca8579b9e5e84	MathLLMs/MathVision	MathLLMs	{"license": "mit", "annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "task_categories": ["question-answering", "multiple-choice", "visual-question-answering", "text-generation", "image-to-text", "image-text-to-text"], "language": ["en"], "tags": ["mathematics", "...	false	False	2026-04-24T10:03:24	140	9	false	f7c403b7f3ec24a162c8b6e2c6a294885c352cf3	Measuring Multimodal Mathematical Reasoning with the MATH-Vision Dataset [💻 Github] [🌐 Homepage] [📊 Main Leaderboard ] [📊 Open Source Leaderboard ] [🌿 Wild Leaderboard ] [🔍 Visualization] [📖 Paper] 🌿 NEW: MATH-Vision-Wild MATH-Vision-Wild is a photographic, real-world variant of MATH-Vi...	18,521	265,396	116,302,571	[ "task_categories:question-answering", "task_categories:multiple-choice", "task_categories:visual-question-answering", "task_categories:text-generation", "task_categories:image-to-text", "task_categories:image-text-to-text", "annotations_creators:expert-generated", "annotations_creators:found", "lang...	2024-02-22T19:14:42	null	null
67afd31dba726eda5c0846dc	google/smol	google	{"license": "cc-by-4.0", "task_categories": ["translation"], "pretty_name": "Smol", "size_categories": ["10K<n<100K"], "language": ["aa", "ab", "abq", "ace", "ach", "ady", "aeb", "af", "ahr", "aii", "ak", "alz", "am", "apc", "apd", "ar", "arn", "arz", "as", "av", "awa", "ay", "ayl", "ba", "bal", "ban", "bbc", "bci", "b...	false	False	2026-04-28T22:59:31	103	9	false	59fd221f9151af49a2d3d5e9c5d3835a7d9eec5a	SMOL SMOL (Set for Maximal Overall Leverage) is a collection professional translations into 221 Low-Resource Languages, for the purpose of training translation models, and otherwise increasing the representations of said languages in NLP and technology. Please read the SMOL Paper and the GATITOS Paper for a ...	2,778	32,916	591,127,623	[ "task_categories:translation", "language:aa", "language:ab", "language:abq", "language:ace", "language:ach", "language:ady", "language:aeb", "language:af", "language:ahr", "language:aii", "language:ak", "language:alz", "language:am", "language:apc", "language:apd", "language:ar", "...	2025-02-14T23:34:53	null	null
68ae11cd78570b7e4c66edba	ScaleAI/SWE-bench_Pro	ScaleAI	{"dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "requirements", "dtype":...	false	False	2026-02-23T20:54:47	102	9	false	7ab5114912baf22bb098818e604c02fe7ad2c11f	Dataset Summary SWE-Bench Pro is a challenging, enterprise-level dataset for testing agent ability on long-horizon software engineering tasks. Paper: https://static.scale.com/uploads/654197dc94d34f66c0f5184e/SWEAP_Eval_Scale%20(9).pdf See the related evaluation Github: https://github.com/scaleapi/SWE-bench_P...	59,252	983,167	7,822,488	[ "benchmark:official", "benchmark:eval-yaml", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2025-08-26T19:58:05	null	null
69df9a007c738fcf8011720d	google/RSRCC	google	{"pretty_name": "RSRCC", "language": ["en"], "task_categories": ["visual-question-answering", "image-text-to-text", "multiple-choice"], "tags": ["remote-sensing", "geospatial", "image", "text", "multimodal", "change-detection", "semantic-change-captioning", "visual-question-answering"]}	false	False	2026-04-23T07:51:57	30	9	false	7898de7bfd08bc404d9a92e1caaa9dce91b0c3ea	RSRCC (A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking) This repository hosts the RSRCC dataset introduced in RSRCC paper. The dataset is designed for semantic change understanding in remote sensing, pairing multi-temporal image evidence wi...	6,289	6,289	1,748,085,591	[ "task_categories:visual-question-answering", "task_categories:image-text-to-text", "task_categories:multiple-choice", "language:en", "size_categories:100K<n<1M", "format:imagefolder", "modality:image", "modality:text", "modality:geospatial", "library:datasets", "library:mlcroissant", "arxiv:26...	2026-04-15T14:00:32	null	null
698b2c8b4c9e577aa3b1fa16	nohurry/Opus-4.6-Reasoning-3000x-filtered	nohurry	{"license": "apache-2.0"}	false	False	2026-03-31T12:43:36	566	8	false	1cd388e9e1172066092a2b53e33dbdd3249b77bd	[!WARNING] NOTICE: The original dataset has been updated with better filtering. Please use the original dataset, not this one. Filtered from: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3000x The original dataset has 979 refusals, I removed these in this version.	9,137	18,211	7,504,789	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-02-10T13:03:07	null	null
698e4ad0913c4d1f4a64479a	Crownelius/Opus-4.6-Reasoning-3300x	Crownelius	{"license": "apache-2.0"}	false	False	2026-04-16T05:11:35	292	8	false	7c60afbc57b339055e1140ffbfafe034a2e4be1f	Opus-4.6-Reasoning-3000x (Cleaned) This dataset has been automatically cleaned to remove: Empty or missing responses Responses shorter than 10 characters Refusal responses ("problem is incomplete", "cannot solve", etc.) Responses with no substantive content Responses that just echo the problem Cle...	3,694	6,849	3,745,854	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-02-12T21:49:04	null	null
69e9e1b85d5039e61d98a3bd	WithinUsAI/Opus4.7_thinking_max_distill_god_seed_25k	WithinUsAI	null	false	False	2026-04-23T10:36:57	8	8	false	7fbf05e7a61eddb1472211a9a3b9b683567aea24	null	111	111	104,363,205	[ "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:polars", "library:mlcroissant", "region:us" ]	2026-04-23T09:09:12	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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