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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 | 371 | 248 | 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... | 51,701 | 51,701 | 1,984,405,985 | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
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"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 | 145 | 65 | 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... | 4,398 | 4,398 | 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 | 320 | 41 | 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... | 7,648 | 9,970 | 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 | 268 | 39 | 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... | 8,681 | 8,686 | 1,616,105,008 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"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 | 43 | 36 | 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... | 6,699 | 6,699 | 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 |
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"], "size_categories": ["1M<n<10M"]} | false | False | 2026-04-27T14:09:08 | 32 | 32 | false | cc8b7066277179f983886662dab5612d1c781183 |
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... | 200 | 200 | 19,552,366,833 | [
"task_categories:text-generation",
"language:en",
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"format:parquet",
"modality:text",
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"library:mlcroissant",
"region:us",
"agent",
"code",
"agentic-traces",
"reinforcement-learning",
... | 2026-04-27T13:14:25 | 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 | 31 | 31 | 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... | 994 | 994 | 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 |
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 | 34 | 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... | 2,435 | 2,435 | 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 | 35 | 27 | 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... | 11,446 | 11,448 | 738,145,122 | [
"task_categories:question-answering",
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"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 |
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 | 62 | 25 | 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... | 6,059 | 10,875 | 433,544,195 | [
"task_categories:text-generation",
"language:en",
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"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 |
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 | 2026-04-29T15:05:22 | 26 | 25 | false | f66cca95b14caaa4177f7ed5eaa424608dadcffa |
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... | 1,244 | 1,244 | 12,794,663,592 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1M<n<10M",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2604.20779",
"region:us",
"code",
"agent",
"trace... | 2026-04-24T15:36:58 | 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 | 52 | 20 | 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... | 3,326 | 11,392 | 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 |
69e4aa7ea8ad7ec14c63ae71 | Roman1111111/claude-sonnet-4.6-120000x | Roman1111111 | null | false | False | 2026-04-19T10:59:32 | 59 | 18 | 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... | 4,064 | 4,064 | 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 |
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 | 103 | 17 | 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... | 20,105 | 20,105 | 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 |
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 | 55 | 15 | 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... | 100,054 | 916,828 | 2,096,790 | [
"benchmark:official",
"benchmark:eval-yaml",
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-04-29T20:42:32 | 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", "deepseek-v4-pro"], "size_categories": ["n<1K"]} | false | False | 2026-05-01T08:56:53 | 16 | 15 | false | ca256e688691328e2b58f132c8034034bcfad988 |
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... | 500 | 500 | 27,776,838 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
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"modality:text",
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"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"distillation",
"chain-of-thought",
"deepseek",
"... | 2026-04-24T03:51:49 | null | null |
69b3fa8c8dd0cb1205153394 | TAAC2026/data_sample_1000 | TAAC2026 | {"license": "cc-by-nc-4.0", "tags": ["TAAC2026", "recommendation"]} | false | False | 2026-04-10T09:07:28 | 74 | 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.... | 11,723 | 17,170 | 40,274,629 | [
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
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"library:polars",
"library:mlcroissant",
"region:us",
"TAAC2026",
"recommendation"
] | 2026-03-13T11:52:44 | 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 | 28 | 13 | 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... | 879 | 879 | 19,210,087 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"chain-of-thought",
... | 2026-04-18T00:05:50 | 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 | 1,049 | 12 | 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... | 405,938 | 6,743,185 | 5,835,742,481,176 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
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"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
... | 2024-05-28T14:32:57 | 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 | 298 | 12 | 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... | 11,915 | 121,537 | 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 |
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... | 200,660 | 561,679 | 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 |
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 | 12 | 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
... | 759 | 759 | 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 |
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 | 37 | 12 | 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... | 1,000 | 1,000 | 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 |
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 | 1,288 | 11 | 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... | 871,250 | 11,023,453 | 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 |
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 | 25 | 11 | 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... | 21,819 | 150,229 | 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 |
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 | 28 | 11 | 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... | 1,886 | 3,298 | 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 |
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 | 104 | 10 | 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... | 37,647 | 988,691 | 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 |
69ef7584836c35985b480a85 | open-thoughts/TaskTrove | open-thoughts | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["agent", "code", "agentic-tasks", "harbor", "reinforcement-learning", "swe-bench"], "size_categories": ["100K<n<1M"]} | false | False | 2026-04-29T14:57:03 | 10 | 10 | false | 5bf429f5ff2644f673419a3a74760ebd67e4a625 |
TaskTrove
TaskTrove is an open-source collection of agentic task datasets, released by the OpenThoughts-Agent team. It contains over 750,000 unique tasks drawn from over 100 task sources, including popular RL and SFT training targets such as SWE-Smith, R2EGym, and SWE-Re-Bench.
TaskTrove is the task compleme... | 60 | 60 | 2,792,238,619 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent",
"code",
"agentic-tasks",
"harbor",
"reinforcement-l... | 2026-04-27T14:41:08 | null | 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 | 94 | 9 | 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... | 6,034 | 52,411 | 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 |
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 | 44 | 9 | 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... | 2,638 | 2,638 | 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 |
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 | 12 | 9 | 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... | 1,928 | 1,928 | 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 |
627007d3becab9e2dcf15a40 | ILSVRC/imagenet-1k | ILSVRC | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["... | false | auto | 2025-09-17T04:58:55 | 787 | 8 | false | 49e2ee26f3810fb5a7536bbf732a7b07389a47b5 |
Dataset Card for ImageNet
Dataset Summary
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than... | 114,943 | 1,884,361 | 166,753,325,463 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"fo... | 2022-05-02T16:33:23 | imagenet-1k-1 | null |
655100ea2adb0688a0042ddd | teknium/OpenHermes-2.5 | teknium | {"language": ["eng"], "pretty_name": "OpenHermes 2.5", "tags": ["synthetic", "GPT-4", "Distillation", "Compilation"]} | false | False | 2024-04-15T08:18:12 | 826 | 8 | false | b82037821055c377bed0d495e72e46de3bc72e84 |
Dataset Card for Dataset Name
This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models.
Support me on GitHub sponsors <3 : https://github.com/sponsors/teknium1
Dataset Details
Dataset Description
The Open Hermes 2/2.5 and Nous Hermes 2 models have made significan... | 22,852 | 216,051 | 1,936,283,760 | [
"language:eng",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"synthetic",
"GPT-4",
"Distillation",
"Compilation"
] | 2023-11-12T16:44:26 | null | null |
End of preview. Expand in Data Studio
Changelog
NEW Changes March 11th 2026
- Added new split:
arxiv_papers, sourced from the Hugging Face/api/papersendpoint paperscontinues to point todaily_papers.parquet, which is the Daily Papers feed
NEW Changes July 25th
- added
baseModelsfield 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
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks ✅
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
Updated Daily
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