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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6a2cd0828137fb18cecbcc06 | Glint-Research/Fable-5-traces | Glint-Research | {"license": "agpl-3.0", "pretty_name": "Fable 5 Pi Agent Traces", "annotations_creators": ["machine-generated"], "language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["agent-traces", "pi-agent", "claude-code", "fable-5", "chain-of-thought", "tool-use", "coding-agents", "s... | false | False | 2026-06-19T01:28:44 | 417 | 108 | false | 3e6e668a6674427a595d3719b716adb2496946a2 |
Glint Research Dataset Card
Fable 5 Pi Agent Traces
A compact, high-signal corpus of Fable 5 coding-agent traces converted into Hugging Face Agent Traces / Pi-compatible sessions for Data Studio inspection, tool-use policy learning, and reasoning/action distillation.
... | 29,791 | 29,791 | 187,507,932 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
... | 2026-06-13T03:37:38 | null | null |
69fa9e0468659d62c5c9df7b | LocalLaws/LOCUS-v1 | LocalLaws | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "pretty_name": "LOCUS v1.0", "tags": ["law", "legal-nlp", "local-government", "municipal-law", "ordinances", "classification"], "configs": [{"config_name": "default", "data_files": [{"split": "tr... | false | False | 2026-06-20T03:06:10 | 56 | 50 | false | 4cee954ca8ad8e31cb0502dff6682c87b74b4302 |
LOCUS v1.0
This repository contains the dataset presented in the paper Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States.
Dataset Summary
LOCUS v1.0 is a chunk-level dataset of U.S. municipal and county law text labeled by legal function. Each eligible chunk is ass... | 1,443 | 1,698 | 1,765,908,117 | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.19334",
"reg... | 2026-05-06T01:48:52 | null | null |
6a2a47c4f5ff6c6dee016974 | armand0e/claude-fable-5-claude-code | armand0e | {"pretty_name": "claude-fable-5 Agent Traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "claude", "distillation", "claude-fable-5", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-06-19T16:23:10 | 206 | 47 | false | c19fb6831700da833b22d1c9cdac47fe8603685c |
claude-fable-5 Agent Traces
It's worth noting that our team was working with Glint-Research to collect as much fable data as possible.
These are just the anonymized raw traces of both of our teams combined. This means that Glint-Research/Fable-5-traces was created from formatting and splitting up this sa... | 10,813 | 10,813 | 75,140,629 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"format:agent-traces",
"claude",
"distillation",... | 2026-06-11T05:29:40 | null | null |
6a2c5668f7f66fcaa0d54e17 | lazarus19/Vibe-Coding-Instruct | lazarus19 | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["custom", "vibecodinginstruct"], "pretty_name": "Vibe-Coding-Instruct", "size_categories": ["1M<n<10M"]} | false | False | 2026-06-18T13:52:24 | 165 | 37 | false | 7ad49b3cbf0b73934b1d567d2b5c4768bce7989e | null | 2,070 | 2,070 | 458,936,450 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"custom",
"vibecodinginstruct"
] | 2026-06-12T18:56:40 | null | null |
6a394ba974e3ccb07645f8a7 | Qwen/AgentWorldBench | Qwen | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["world-model", "agent", "benchmark", "evaluation", "environment-simulation", "qwen"], "size_category": "1K<n<10K"} | false | False | 2026-06-24T02:05:57 | 33 | 32 | false | db74b0cca6f7dd41b9684b18a3633d13f2bbf783 |
AgentWorldBench
AgentWorldBench is a comprehensive evaluation benchmark for language world models, constructed from real-world observations of frontier model trajectories on established benchmarks such as Tool Decathlon, Terminal-Bench 1.0 & 2.0, and OSWorld-Verified. Every evaluation sample is paired wi... | 478 | 478 | 257,212,028 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.24597",
"region:us",
"world-model",
"agent",
... | 2026-06-22T14:50:17 | null | null |
6a06cc4dea20d325f8fc6213 | ArtificialAnalysis/ITBench-AA | ArtificialAnalysis | {"license": "cc-by-4.0", "language": ["en"], "task_categories": ["question-answering"], "tags": ["sre", "kubernetes", "root-cause-analysis", "agents", "it-operations"], "pretty_name": "ITBench-AA", "size_categories": ["n<1K"], "configs": [{"config_name": "sre", "data_files": [{"split": "test", "path": "sre/data.jsonl"}... | false | False | 2026-05-27T01:28:25 | 38 | 31 | false | 76df38a82288f75ba9e41dc8c515033332497473 |
ITBench-AA
Artificial Analysis' release of the public scenarios from
IBM's ITBench benchmark, used for
the ITBench-AA leaderboard.
This repo currently contains the SRE subset (sre config). Each row is a
Kubernetes incident scenario with its expected contributing-factor entities. An
agent under evaluation... | 30,052 | 30,055 | 31,095,271,891 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"sre",
"kubernetes",
"root-cause-analysis",
"agents",
"it-operat... | 2026-05-15T07:33:33 | null | null |
6a05fb804b04c5157df46866 | WithinUsAI/claude_mythos_distilled_25k | WithinUsAI | {"license": "apache-2.0", "language": ["en"], "tags": ["synthetic", "claude", "mythos", "distillation", "cybersecurity", "coding", "reasoning", "agentic", "frontier-model-mirror", "sft", "instruction-tuning"], "size_categories": ["10K<n<100K"], "pretty_name": "Claude Mythos Distilled 25K", "dataset_info": {"features": ... | false | False | 2026-05-18T00:45:03 | 122 | 30 | false | 2c5e638c51a22b8b883def51bab685ae7e282c72 |
Claude Mythos Distilled 25K
A high-quality synthetic supervised fine-tuning (SFT) dataset designed to train and fine-tune any LLM to mirror the capabilities, reasoning style, agentic behavior, and technical depth of Anthropic's Claude Mythos (distilled frontier model).
Dataset Summary
Siz... | 3,061 | 3,358 | 55,202,753 | [
"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",
"synthetic",
"claude",
"mythos",
"distillation",
"cybersecurity",
"coding",
"reasoning",
"a... | 2026-05-14T16:42:40 | null | null |
6a307dae8e258cbed418ec58 | XDOF/ABC-130k | XDOF | {"license": "apache-2.0", "pretty_name": "ABC", "language": ["en"], "tags": ["robotics", "manipulation", "imitation-learning", "bimanual", "teleoperation", "mcap"], "task_categories": ["robotics"], "size_categories": ["n>1T"], "configs": [{"config_name": "yam", "data_files": [{"split": "train", "path": "data/train/**"}... | false | auto | 2026-06-22T17:53:22 | 55 | 25 | false | 071311db1ac281848714bff024f9c6f944837c40 |
ABC-130k
ABC-130k is the largest open-source robot teleoperation dataset. It contains
bimanual manipulation trajectories collected on two-arm YAM stations. Episodes
are distributed as MCAP files, with subtask annotations kept as separate
artifacts so they can be revised or extended independently of the u... | 339,698 | 339,698 | 22,101,486,810,360 | [
"task_categories:robotics",
"language:en",
"license:apache-2.0",
"size_categories:n>1T",
"region:us",
"robotics",
"manipulation",
"imitation-learning",
"bimanual",
"teleoperation",
"mcap"
] | 2026-06-15T22:33:18 | 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 | 418 | 24 | 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 ... | 9,395 | 15,901 | 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 |
6a27d0ad7a6ecef661d66995 | BitRobot/HIW-500 | BitRobot | {"pretty_name": "HIW500: Humanoids In-the-Wild Dataset", "language": ["en"], "tags": ["robotics", "humanoid"], "license": "cc-by-4.0"} | false | False | 2026-06-25T09:24:44 | 23 | 23 | false | 07c1bc714eea0d58f6da5b8f923cdafa6987889b |
HIW500: Humanoids In-the-Wild Dataset
https://bitrobot-foundation.github.io/humanoids-in-the-wild-500-hours/
HIW500: Humanoids In-the-Wild Dataset is a large-scale dataset for whole-body humanoid robot learning in natural home environments. It captures human teleoperation demonstrations on Unitree G1 acr... | 30,768 | 30,768 | 8,937,650,917,107 | [
"language:en",
"license:cc-by-4.0",
"region:us",
"robotics",
"humanoid"
] | 2026-06-09T08:37:01 | null | null |
67ac9b0ae2c56194379f17a9 | SakanaAI/AI-CUDA-Engineer-Archive | SakanaAI | {"tags": ["code"], "pretty_name": "The AI CUDA Engineer Archive", "license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "level_1", "path": "level_1.parquet"}, {"split": "level_2", "path": "level_2.parquet"}, {"split": "level_3", "path": "level_3.parquet"}]}]} | false | False | 2025-02-20T02:02:27 | 221 | 18 | false | 4edbe8d6d0b417e05aaf8ec7e23f78aecdc5516b |
The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition
We release The AI CUDA Engineer archive, a dataset consisting of approximately 30,000 CUDA kernels generated by The AI CUDA Engineer. It is released under the CC-By-4.0 license and can be accessed via HuggingFace ... | 3,279 | 31,123 | 67,716,683 | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code"
] | 2025-02-12T12:58:50 | null | null |
6a2d8bf9763f90e1368360cb | lordx64/agentic-distill-fable-5-sft | lordx64 | {"license": "agpl-3.0", "language": ["en"], "tags": ["agentic", "chain-of-thought", "distillation", "claude", "claude-fable-5", "agent-traces", "sft", "qwen-chat-template", "qwable"], "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split":... | false | False | 2026-06-15T14:15:12 | 39 | 17 | false | 9df06dd13b692dd482bd6ef0e547f577a5f94942 |
Fable-5 SFT — prepared for Qwable fine-tuning
4,659 single-turn pairs from Claude Fable-5 (Anthropic preview model, suspended globally 2026-06-22 under U.S. export-control directives), reformatted into a single-text-column parquet ready for SFTTrainer(dataset_text_field="text") + train_on_responses_only.... | 946 | 946 | 14,605,136 | [
"task_categories:text-generation",
"language:en",
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"agentic",
"chain-of-thought",
"distillation",
"claude",
"cla... | 2026-06-13T16:57:29 | null | null |
6a34e9d01b6b6e116d313e13 | Crownelius/Complete-FABLE.5-traces-2M | Crownelius | {"license": "mit", "pretty_name": "Complete FABLE.5 Traces 2M", "annotations_creators": ["machine-generated"], "language": ["en"], "language_creators": ["found", "machine-generated"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "task_ids": ["language-mode... | false | False | 2026-06-21T12:26:51 | 20 | 17 | false | 19a5b7863e10eec6838cf531bd20d24d2ec1106e |
Complete FABLE.5 Traces 2M
Full FABLE.5 / Mythos corpus restored, with session-limit answer rows removed.
Dataset Viewer | Parquet | Raw JSONL.gz
This dataset is a post-closure compilation of all available FABLE.5 / Mythos trace datasets found on Hugging Face during the curation pass after the ... | 1,349 | 1,349 | 2,079,549,297 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabula... | 2026-06-19T07:03:44 | null | null |
6a3404497e03daf35bd3202e | scholarweave/arxiv-latex | scholarweave | {"license": "other", "license_name": "dual-license", "license_link": "LICENSE", "task_categories": ["text-generation", "feature-extraction"], "language": ["en"], "tags": ["science", "arxiv", "latex", "academic"], "pretty_name": "arXiv LaTeX Source Dataset", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "... | false | False | 2026-06-25T10:46:32 | 16 | 16 | false | 9e402d14e2ef8187b0d29f646feeb9693e6b0d0e |
arXiv LaTeX Source Dataset
This dataset provides the entire corpus of arXiv's LaTeX source files, pre-parsed, formatted, and aligned with official metadata in ready-to-query Parquet files.
Why I Built This
If you have ever tried to work with the complete history of arXiv papers at scale, ... | 801 | 801 | 196,218,171,651 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"science",
"arxiv",
"latex",
... | 2026-06-18T14:44:25 | null | null |
67d45c3d35fc7f6d2ab224c8 | allenai/olmOCR-bench | allenai | {"license": "odc-by", "tags": ["text"], "configs": [{"config_name": "olmocr-bench", "data_files": [{"split": "arxiv_math", "path": ["bench_data/arxiv_math.jsonl"]}, {"split": "headers_footers", "path": ["bench_data/headers_footers.jsonl"]}, {"split": "long_tiny_text", "path": ["bench_data/long_tiny_text.jsonl"]}, {"spl... | false | False | 2026-02-19T17:28:38 | 245 | 15 | false | 54a96a6fb6a2bd3b297e59869491db4d3625b711 |
olmOCR-bench
olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have.
This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and str... | 5,817 | 49,495 | null | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:odc-by",
"size_categories:1K<n<10K",
"modality:document",
"modality:text",
"arxiv:2502.18443",
"region:us",
"text"
] | 2025-03-14T16:41:33 | null | null |
6a35a1b97d1c93c320e0c0d1 | AletheiaResearch/GLM-5.2-Agent | AletheiaResearch | {"pretty_name": "GLM-5.2 Agent traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "pi", "distillation", "z-ai/glm-5.2", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "**/*.jsonl"}]}]} | false | False | 2026-06-23T18:46:49 | 15 | 15 | false | 10f3e37942a1f5abb6b3f04c71886f9a36248fa3 | This dataset was generated using teich by TeichAI
GLM-5.2 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 284
Model metadata: z-ai/glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain ava... | 760 | 760 | 90,472,581 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:eu",
"agent-traces",
"format:agent-traces",
"pi",
"distillation",
"... | 2026-06-19T20:08:25 | null | null |
69af1e7e484ef491320be72e | Aignostics/OpenTME | Aignostics | {"license": "other", "task_categories": ["image-classification", "image-segmentation", "image-feature-extraction", "object-detection"], "tags": ["biomarker-discovery", "biology", "bladder-cancer", "breast-cancer", "cancer", "cell-segmentation", "colorectal-cancer", "computational-pathology", "digital-pathology", "H&E",... | false | manual | 2026-06-19T12:03:22 | 29 | 13 | false | b2e4a22823f8d14d1097d568461afeeb1e5bc67e |
OpenTME: Open-Access Tumor Microenvironment Profiles from TCGA
OpenTME is an open-access project by Aignostics for academic researchers. It provides comprehensive spatial outputs for whole slide images (WSIs) of formalin-fixed, paraffin-embedded slides from The Cancer Genome Atlas (TCGA). OpenTME is powe... | 15,574 | 16,338 | 5,066,213,789 | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"task_categories:image-feature-extraction",
"task_categories:object-detection",
"license:other",
"size_categories:10K<n<100K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2... | 2026-03-09T19:24:46 | null | null |
6a27d2d419ba88e1bcf065fb | BitRobot/HIW-500-LeRobot | BitRobot | {"pretty_name": "HIW500: Humanoids In-the-Wild Dataset (LeRobot)", "language": ["en"], "tags": ["LeRobot", "robotics", "humanoid", "unitree-g1", "manipulation", "mobile-manipulation", "bimanual", "imitation-learning", "teleoperation", "in-the-wild"], "task_categories": ["robotics"], "configs": [{"config_name": "default... | false | False | 2026-06-25T09:25:47 | 13 | 13 | false | d8fc5b6f91df8e4c153ac7ccfd122efd0869cb76 | This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"observation.images.head": {
"dtype": "video",
"shape": [
480,
1280,
3
... | 11,181 | 11,181 | 2,150,165,807,005 | [
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:lerobot",
"r... | 2026-06-09T08:46:12 | null | null |
6a3246e7ae94378f6d10aff0 | PawanKrd/claude-fable-5-code | PawanKrd | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "code.fable-5.jsonl"}]}], "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "model", "dtype": "stri... | false | False | 2026-06-17T07:37:06 | 18 | 13 | false | 4bf63f6009a984b50f5a7e07368e3fe24fa849aa |
Claude Fable 5 Coding and Math Dataset (Non-Thinking)
This repository contains a dataset of 603 coding and math-related prompts and responses from Claude Fable 5.
The generation of this dataset cost approximately $75.
Please note that this dataset is non-thinking. Fable 5 only supported adaptive thinking... | 442 | 442 | 3,173,322 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"claude",
"fable-5"
] | 2026-06-17T07:04:07 | null | null |
6a36e65ae86005a76f1c7adf | ajibawa-2023/Shell-Code-Large | ajibawa-2023 | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["Shell", "Code", "LLM", "Training"], "size_categories": ["100K<n<1M"]} | false | False | 2026-06-20T19:48:46 | 14 | 13 | false | 91adad625cc7d91ce983f95466e1bbfb2693fd87 |
Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud i... | 146 | 146 | 3,365,721,917 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"Shell",
"Code",
"LLM",
"Training"
] | 2026-06-20T19:13:30 | null | null |
685d6910fa047a479aefe2a2 | ibm-research/AssetOpsBench | ibm-research | {"language": ["en"], "pretty_name": "AssetOpsBench", "configs": [{"config_name": "scenarios", "data_files": [{"split": "train", "path": "data/scenarios/all_utterance.jsonl"}], "default": true}, {"config_name": "compressor", "data_files": [{"split": "train", "path": "data/asset/compressor_utterance.jsonl"}]}, {"config_n... | false | False | 2026-05-28T17:10:58 | 43 | 12 | false | 5e25bb7f2cd37fb68b9a9e1f99d170ca5be7ce17 |
AssetOpsBench
AssetOpsBench is a specialized benchmark designed for evaluating Large Language Models (LLMs) and Multi-Agent systems in industrial operations. It focuses on the intersection of sensor data interpretation, maintenance logic, and Prognostics and Health Management (PHM).
The benchmark enables... | 873 | 9,848 | 10,034,194 | [
"task_categories:question-answering",
"task_categories:time-series-forecasting",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2506.03828",
"region:us",
"Ind... | 2025-06-26T15:36:48 | null | null |
69a88758b1a7a119a30f88c2 | ibm-research/ScarfBench | ibm-research | {"task_categories": ["text-generation"], "tags": ["code", "benchmark", "evaluation", "java", "code-translation", "agentic"], "pretty_name": "Scarf Benchmark"} | false | False | 2026-05-22T13:23:33 | 16 | 12 | false | 82de5bc7330dad7470ad0320c5774437cb83e423 |
Scarf (Self-Contained Application Refactoring) is a benchmark suite for evaluating AI agents' ability to migrate enterprise Java applications across Jakarta EE, Quarkus, and Spring while preserving functionality, idiomatic patterns, and architectural integrity.
Applications
Layers
Frameworks
Tests
... | 485 | 3,488 | 151,945,534 | [
"task_categories:text-generation",
"arxiv:2605.06754",
"region:us",
"code",
"benchmark",
"evaluation",
"java",
"code-translation",
"agentic"
] | 2026-03-04T19:26:16 | null | null |
6a2c0ff5f05071e5d8d863dd | makora-ai/triton-gpu-latency | makora-ai | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["gpu", "triton", "cuda", "kernel-generation", "code", "pytorch", "latency", "benchmark"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train.parquet"... | false | False | 2026-06-12T15:14:03 | 12 | 12 | false | 3b30911350fa433e1cb97ec2a41bd8908dd35d5d |
Triton GPU Latency Dataset
A large dataset of PyTorch problems (mostly from KernelBench) paired with candidate Triton-kernel implementations and their measured GPU runtimes generated by MakoraGenerate. Each row is a self-contained Python program that defines (1) a reference Model written with plain PyTor... | 110 | 110 | 1,968,266,807 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"gpu",
"triton",
"cuda",
"kernel-generation",
"code",
"p... | 2026-06-12T13:56:05 | null | null |
6a3a711102e6e7f9c77f3a2a | Rapidata/svg-benchmark | Rapidata | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cc-by-4.0", "task_categories": ["text-to-image", "image-classification", "reinforcement-learning"], "language": ["en"], "size_categories": ["100K<n<1M"], "pretty_name": "SVG Generation Benchmark (Static)", ... | false | False | 2026-06-25T13:49:04 | 12 | 12 | false | 2a749cd790a5853e411b41ead7614b270d6b89bf |
Rapidata Static SVG Generation Benchmark
Built by Rapidata.
This dataset contains 1,355,161 human responses, collected with the
Rapidata Python SDK, comparing how well 30 frontier LLMs generate
static SVGs from text prompts. Each row is a head-to-head comparison between two models' renders of
the same pr... | 119 | 119 | 24,583,751,740 | [
"task_categories:text-to-image",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask... | 2026-06-23T11:42:09 | null | null |
69e15643062441e6b7109caa | nvidia/Open-SWE-Traces | nvidia | {"dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "trajectory_id", "dtype": "string"}, {"name": "trajectory", "list": [{"name": "role", "dtype": "string"}, {"name": "co... | false | False | 2026-06-25T16:48:33 | 24 | 11 | false | f6689f56f1af2e2082861738071d4c4278b1922a |
Open-SWE-Traces: Advancing Distillation for Software Engineering Agents
Data Overview
Open-SWE-Traces is an agentic instruction tuning dataset designed to advance the capabilities of LLMs in software engineering. This dataset comprises 200k+ agent
trajectories collected using the SWE-agen... | 1,936 | 1,954 | 17,783,617,931 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2606.16038",
"region:us",
"code",
"synthetic",
"tools",
"agents",
"software"
] | 2026-04-16T21:36:03 | null | null |
6a3154f1671ba44c169ee371 | google/WikiProfile | google | {"license": "cc-by-sa-4.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["Factuality", "Knowledge", "Wikipedia", "QA"], "pretty_name": "wikiprofile", "size_categories": ["1K<n<10K"]} | false | False | 2026-06-19T09:58:02 | 12 | 11 | false | 0448b6abf3d6fb0a964e6935c0265dbd47584cda |
WikiProfile
WikiProfile is a factual knowledge benchmark for evaluating how well language models encode and recall factual knowledge. It comprises 2,150 facts, each paired with 10 questions, for a total of 21,500 question instances.
Each fact is grounded in the first paragraph (summary) of an English Wik... | 246 | 246 | 7,510,683 | [
"task_categories:question-answering",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.14080",
"region:us",
"Factuality",
"Knowl... | 2026-06-16T13:51:45 | 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 | 2,902 | 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 ... | 284,094 | 8,550,468 | 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 |
67d05b35637847cd702f212e | futo-org/swipe.futo.org | futo-org | {"language": ["en"], "license": "mit", "task_categories": ["other"], "configs": [{"config_name": "swipe-1", "data_files": [{"split": "train", "path": "train.jsonl"}, {"split": "test", "path": "test.jsonl"}, {"split": "validation", "path": "dev.jsonl"}]}, {"config_name": "swipe-2", "data_files": [{"split": "train", "pat... | false | False | 2026-06-25T20:28:18 | 26 | 10 | false | d71bf5fd7f45b3e7c2ed2d76a21b0dbd3b4ba566 |
Dataset Card for swipe.futo.org
This dataset is presented in the paper FUTO Swipe: Layout-Agnostic Neural Swipe Decoding.
It contains multiple collection runs from the swipe.futo.org website. The QWERTY layout definition is provided here
Collection process
Users were able to volunteer to c... | 446 | 2,889 | 6,872,036,572 | [
"task_categories:other",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.25247",
"region:us"
] | 2025-03-11T15:48:05 | null | null |
6a35605763a799204f741823 | jdopensource/JoyAI-VL-Interaction | jdopensource | {"license": "apache-2.0", "task_categories": ["video-text-to-text"]} | false | False | 2026-06-23T08:06:07 | 11 | 10 | false | 6cf9b8383e0fcfeecfe8c12d44499b01b931c98e |
JoyAI-VL-Interaction Dataset
The first open, vision-driven real-time interaction model — it watches a live video stream and decides on its own when to speak, stay silent, or delegate.
📄 Paper · 🌐 Project Page & Demos · 💻 GitHub · 🤗 Paper Page
Dataset Description
This repository contai... | 645 | 645 | 2,647,458,242 | [
"task_categories:video-text-to-text",
"license:apache-2.0",
"arxiv:2606.14777",
"region:us"
] | 2026-06-19T15:29:27 | 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 | 1,404 | 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... | 897,775 | 12,808,188 | 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 |
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 | 2026-06-22T15:37:31 | 508 | 9 | false | ada0f5b53a38bb5a30cce09358adde883c1ab63a |
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 반영... | 13,071 | 103,746 | 1,984,406,022 | [
"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 |
6a00ee1b8af2b11e0d2b374b | WithinUsAI/GPT_5.5_Distilled | WithinUsAI | {"license": "apache-2.0"} | false | False | 2026-05-12T17:49:04 | 21 | 9 | false | 4f49e8c7e98ca80694b7378ff8fed5f7344c5fb3 | null | 870 | 1,022 | 46,711,856 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-05-10T20:44:11 | 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 | 141 | 8 | 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... | 68,729 | 1,105,972 | 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 |
69693d3f3669d609ad898396 | ibm-research/ITBench-Lite | ibm-research | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "scenario_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "task_type", "dtype": "string"}, {"name": "snapshot_data", "dtype": "object"}], "configs": [{"config_name": "sre", "data_files": [{"... | false | False | 2026-04-21T15:30:08 | 12 | 8 | false | d0916b08ba421ce5e672e9ad68aa947d938dfef0 |
ITBench-Lite Dataset Card
Dataset Overview
Dataset Name: ITBench-LiteOrganization: IBM ResearchLicense: Apache 2.0Language: EnglishPaper: ITBench: Evaluating AI Agents across Diverse Real-World IT Automation TasksGitHub: ITBench
ITBench-Lite is a systematic framework for benchmarking LLMs and AI... | 9,171 | 21,729 | 31,034,154,126 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:other",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"arxiv:2502.05352",
"region:us",
"kubernetes",
"sre",
"finops",
"ciso",
"incident-response",
"fault-diagnosis",
"root-cause... | 2026-01-15T19:17:19 | null | null |
6a261e159c9c7a503bfdea7c | open-thoughts/OpenThoughts-Agent-SFT-100K | open-thoughts | {"license": "apache-2.0", "language": ["en"], "tags": ["agents", "terminal", "code", "software-engineering", "sft"], "pretty_name": "OpenThoughts-Agent-SFT-100K", "size_categories": ["100K<n<1M"]} | false | False | 2026-06-08T07:01:02 | 8 | 8 | false | 45fb28fcc38d352133cb28a1c8a43a2f14fea97b |
Project |
Code |
Collection
OpenThoughts-Agent-SFT-100K
OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes datasets, models and our research codebase.
OpenThoughts-Agent-SFT-100K is the 100,000-example point of the OpenThoughts-Ag... | 251 | 251 | 1,749,504,790 | [
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agents",
"terminal",
"code",
"software-engineering",
"sft"
] | 2026-06-08T01:42:45 | null | null |
6a313297671ba44c169b69c0 | HKUSTAudio/ISCSLP2026-CoT-TTS | HKUSTAudio | {"viewer": false} | false | False | 2026-06-19T02:00:44 | 9 | 8 | false | 29e5d4b3232398763ca6b42ec0c6025f1c9d7e8a |
ISCSLP 2026 CoT-TTS Dataset
Dataset Overview
This dataset is prepared for the ISCSLP 2026 CoT-TTS Challenge and is designed to support research on context-aware, expressive, and CoT-guided speech generation. It is constructed from speech-rich media sources, including films, TV dramas, radi... | 7,708 | 7,708 | 2,152,290,152,143 | [
"region:us"
] | 2026-06-16T11:25:11 | null | null |
6a31fb7d840df2d57f83c572 | nvidia/Nemotron-Personas-Belgium | nvidia | {"license": "cc-by-4.0", "language": ["nl", "fr", "de", "en"], "task_categories": ["text-generation"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner", "belgium", "Dutch", "French", "German", "English"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"nam... | false | False | 2026-06-17T05:12:10 | 32 | 8 | false | b13368c38c5667c9b8b035accaf0d2b3298b38b3 |
Nemotron-Personas-Belgium
(NL) Een compound-AI-benadering van meertalige Belgische persona's, verankerd in reële verdelingen
(FR) Une approche d'IA composée pour des personas belges multilingues, ancrés dans des distributions réelles
(DE) Ein Compound-KI-Ansatz für mehrsprachige belgis... | 2,167 | 2,167 | 4,023,924,923 | [
"task_categories:text-generation",
"language:nl",
"language:fr",
"language:de",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"regio... | 2026-06-17T01:42:21 | null | null |
6a347406d390563cd60d032d | allenai/tmax-15k-open-instruct | allenai | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "ground_truth", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "env_config", "struct": [{"name": "env_name", "dtype": "string"}, {"name": "image", "d... | false | False | 2026-06-23T03:28:43 | 8 | 8 | false | 41ec44bb6c2240aa74e6f640a15948782427355c |
💻 Code ·
🤗 Models & Data ·
📜 Paper ·
📓 Blog
[!NOTE]
For full information, go check out the Tmax paper here.
TMax 15k - Open Instruct
This is the dataset we used to train Tmax 9b (and our other tmax models), formatted for use with our open-instruct fork here.
In general, this is a collec... | 397 | 397 | 49,018,770 | [
"language:en",
"license:odc-by",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2606.23321",
"region:us"
] | 2026-06-18T22:41:10 | null | null |
6a35f05ccc0bdc9c01db2797 | Infatoshi/kernelbench-hard-traces | Infatoshi | {"license": "mit", "tags": ["agent-traces", "claude", "kernelbench", "gpu-kernels"], "pretty_name": "KernelBench-Hard agent traces", "size_categories": ["n<1K"]} | false | False | 2026-06-23T03:49:50 | 8 | 8 | false | 2e3d4736b9e8e17e5df971c94839331e476a8278 |
KernelBench-Hard agent traces
Frontier coding agents writing optimized CUDA/Triton kernels (FP8 GEMM, paged attention, MoE, W4A16, ...) on RTX PRO 6000 Blackwell, H100 PCIe, and B200; roofline-graded.
Each .jsonl file is one agent run in Claude-Code session format, viewable with the agent trace viewer. F... | 523 | 523 | 66,579,228 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"claude",
"kernelbench",
"gpu-kernels"
] | 2026-06-20T01:43:56 | 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 | 777 | 7 | 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... | 439,737 | 42,038,852 | null | [
"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 |
645e8da96320b0efe40ade7a | roneneldan/TinyStories | roneneldan | {"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2024-08-12T13:27:26 | 1,042 | 7 | false | f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation los... | 79,540 | 1,494,855 | 7,621,978,240 | [
"task_categories:text-generation",
"language:en",
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
656523d6bfb751371817c448 | Idavidrein/gpqa | Idavidrein | {"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extende... | false | auto | 2026-03-05T23:06:58 | 470 | 7 | false | 633f5ee89ab8ad4522a9f850766b73f62147ffdd |
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending ... | 98,671 | 1,850,566 | 8,713,216 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"... | 2023-11-27T23:18:46 | null | null |
6a1cbd0141aa598ff9f9bf57 | HelioAI/Fable-5-Distill-Reasoning-462x | HelioAI | {"annotations_creators": ["machine-generated"], "language": ["en", "ru"], "license": "unknown", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["reasoning", "long-context", "reasoning-traces", "synthetic-data", "chain-of-thought", "process-supervision", "mythos-v2", "deep-reasoning", "trac... | false | False | 2026-06-15T22:35:42 | 31 | 7 | false | ab4e69b74e7ef455f15f23fc60bac891db90a918 |
HelioAI Labs
Mythos V2 Full Distill
DeepReason 462×105M
Unrestricted full-parameter distillation from Mythos V2 — complete reasoning traces with zero alignment truncation, engineered for deep analytical research and process supervision.
... | 1,160 | 1,160 | 146,180,522 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"language:ru",
"license:unknown",
"size_categories:n<1K",
"region:us",
"reasoning",
"long-context",
"reasoning-traces",
"synthetic-data",
"chain-of-thought",
"process-supervision",
"mythos-v2",
"d... | 2026-05-31T22:58:09 | null | null |
6a23971aed8b6eeac4e4fef0 | GenAI4ELab/papercli-papers | GenAI4ELab | {"license": "cc-by-4.0", "pretty_name": "AI Conference & Journal Papers", "configs": [{"config_name": "aaai", "data_files": [{"split": "2026", "path": "browse/aaai/2026.parquet"}, {"split": "2025", "path": "browse/aaai/2025.parquet"}, {"split": "2024", "path": "browse/aaai/2024.parquet"}, {"split": "2023", "path": "bro... | false | False | 2026-06-20T18:14:37 | 17 | 7 | false | 90a1fbd3c355717092966debf5f7f69bdc6a1cf6 |
AI Conference & Journal Papers
Searchable metadata and full-text PDF mirrors for papers from top-tier AI venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, WACV, ACL, EMNLP, NAACL, IJCAI, AAAI, JMLR, Interspeech) from 2023.
📊 papers.parquet: The complete dataset containing all fields and all venues.
🔍 Pe... | 10,743 | 10,743 | 167,540,957 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-06-06T03:42:18 | null | null |
6a3232e527a91971fc536a73 | AutoArk-AI/ark-asr-open-asr-leaderboard-results | AutoArk-AI | {"pretty_name": "ARK-ASR Open ASR Leaderboard Results", "license": "apache-2.0"} | false | False | 2026-06-17T05:39:31 | 12 | 7 | false | 1419570c86ca7599cb25efedee2361de38a77ff4 |
ARK-ASR Open ASR Leaderboard Results
This dataset contains JSONL prediction manifests for AutoArk-AI/ARK-ASR-0.6B on hf-audio/open-asr-leaderboard public English short-form splits.
These files are intended for Open ASR Leaderboard maintainer verification.
Scoring summary from normalizer.eval_utils.score_... | 93 | 93 | 28,171,974 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2026-06-17T05:38:45 | null | null |
6a38b528b22e71da5a862d50 | sequelbox/Titanium4-DeepSeek-V4-Pro | sequelbox | {"license": "apache-2.0", "tags": ["titanium", "titanium-4", "agentic", "agentic-coding", "python", "dev-ops", "devops", "terraform", "ansible", "docker", "jenkins", "kubernetes", "helm", "grafana", "prometheus", "shell", "bash", "azure", "aws", "gcp", "c++", "c#", "c", "rust", "java", "javascript", "typescript", "algo... | false | False | 2026-06-22T04:36:35 | 7 | 7 | false | 4b6b6c1849c757f6071ffc1e2bf4b13ef54e0b2b | Click here to support our open-source dataset and model releases - help us speed up our release schedule!
Titanium 4 is an agentic coding dataset focused on DevOps and architecture, testing the limits of DeepSeek-V4-Pro's agentic skills:
Questions prioritize real-world, challenging agentic coding tasks in DevOps and a... | 93 | 93 | 904,758,228 | [
"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/9257",
"region:us",
"titanium",
"titanium-4",
"agentic",
... | 2026-06-22T04:08:08 | null | null |
6a3907d46e621593a3b86a2d | AutoArk-AI/ark-asr-3b-open-asr-leaderboard-results | AutoArk-AI | {"pretty_name": "ARK-ASR-3B Open ASR Leaderboard Results", "license": "apache-2.0", "task_categories": ["automatic-speech-recognition"], "tags": ["open-asr-leaderboard", "ark-asr", "automatic-speech-recognition"]} | false | False | 2026-06-22T10:01:19 | 11 | 7 | false | b98599d8b525251956c36fd237b0c30af18fea90 |
ARK-ASR-3B Open ASR Leaderboard Results
Raw JSONL manifests for AutoArk-AI/ARK-ASR-3B on the public English
short-form hf-audio/open-asr-leaderboard splits.
These manifests were generated on a local 8x RTX 4090 machine and scored with
the shared Open ASR Leaderboard scorer:
PYTHONPATH=. python - <<'PY'
f... | 88 | 88 | 28,264,125 | [
"task_categories:automatic-speech-recognition",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us",
"open-asr-leaderboard",
"ark-asr",
"automatic-speech-recognition"
] | 2026-06-22T10:00:52 | null | null |
621ffdd236468d709f18200d | Salesforce/wikitext | Salesforce | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-3.0", "gfdl"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-mo... | false | False | 2024-01-04T16:49:18 | 725 | 6 | false | b08601e04326c79dfdd32d625aee71d232d685c3 |
Dataset Card for "wikitext"
Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared t... | 1,310,477 | 33,352,984 | 643,690,517 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0... | 2022-03-02T23:29:22 | wikitext-2 | null |
6532270e829e1dc2f293d6b8 | gaia-benchmark/GAIA | gaia-benchmark | {"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the ... | false | auto | 2025-10-28T14:44:54 | 703 | 6 | false | 682dd723ee1e1697e00360edccf2366dc8418dd9 |
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable fo... | 37,706 | 315,622 | 110,175,514 | [
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null | 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 | 862 | 6 | 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... | 15,498 | 248,532 | 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 |
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 | 1,164 | 6 | 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 ... | 388,897 | 7,716,154 | 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 |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "license": "mit", "viewer": true, "configs": [{"config_name": "release_2026_01_15", "data_files": [{"split": "raw_claude_ai", "path": "release_2026_01_15/data/intermediate/aei_raw_claude_ai_2025-11-13_to_2025-11-2... | false | False | 2026-05-21T05:15:51 | 552 | 6 | false | db51ecb12920faef6df2b21dff6207ebcbc72c6f |
The Anthropic Economic Index
Overview
The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
Data Releases
This repository contains multiple data releases, each with its own documentation:
Labor market impacts: ... | 23,671 | 159,962 | 367,402,241 | [
"language:en",
"license:mit",
"arxiv:2503.04761",
"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | 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
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