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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
69ef6131ceb075c32613a27a | open-thoughts/AgentTrove | open-thoughts | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["agent", "code", "agentic-traces", "reinforcement-learning", "terminus-2", "harbor", "agent-traces"], "size_categories": ["1M<n<10M"]} | false | False | 2026-05-07T14:20:40 | 83 | 47 | false | b395a4307a2bc9950a90dc899438f149e115fc60 |
AgentTrove
AgentTrove is the largest open-source collection of agentic interaction traces to date, released by the OpenThoughts-Agent team. It contains 1,696,847 rows drawn from 219 source datasets spanning code repair, shell scripting, mathematical problem-solving, competitive programming, and general compu... | 6,304 | 6,304 | 19,552,366,847 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent",
"code",
"agentic-traces",
"reinforcement-learning",
... | 2026-04-27T13:14:25 | null | null |
69e695a5d20baec02ee3039c | nvidia/Nemotron-Personas-Korea | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["ko"], "tags": ["synthetic", "personas", "NVIDIA", "Korean", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "s... | false | False | 2026-04-23T07:42:48 | 423 | 38 | 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... | 70,270 | 70,270 | 1,984,405,985 | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
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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 | 183 | 36 | 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... | 7,089 | 7,089 | 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 |
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 | 68 | 31 | 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... | 6,738 | 6,738 | 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 |
69b186f91cde8c71bb8f76b0 | Roman1111111/claude-opus-4.6-10000x | Roman1111111 | {"license": "mit"} | false | False | 2026-04-05T13:42:24 | 350 | 29 | 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... | 7,872 | 11,344 | 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 |
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 | 36 | 29 | false | f0330e0ca46469b3928adef18c2b55f9476d6bd3 |
Background
Ended up with some tokens to burn on a Claude Max plan. Assembly began during 4.6 and moved to 4.7. Model is tagged. The development evolved as it went along. The dataset has not been manually reviewed. It's entirely Claude developed.
Clarification on Reasoning
The reasoning is not Clau... | 773 | 773 | 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 |
69da71d4cf8e40febe35f7b7 | ADSKAILab/Zero-To-CAD-1m | ADSKAILab | {"license": "apache-2.0", "task_categories": ["text-to-3d", "image-to-3d"], "tags": ["CAD", "CadQuery", "synthetic-data", "construction-sequence", "parametric-CAD", "3D-generation", "agentic-AI", "code-generation"], "pretty_name": "Zero-to-CAD 1M", "size_categories": ["1M<n<10M"], "language": ["en", "code"], "configs":... | false | False | 2026-05-03T14:11:21 | 37 | 28 | false | 09dbd1805a5e73a2757f380b93042b8089cd4f3f |
Zero-to-CAD 1M
One million executable, interpretable CAD construction sequences synthesized entirely without real-world data.
Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan, Pradeep Kuma... | 6,984 | 6,984 | 349,104,973,477 | [
"task_categories:text-to-3d",
"task_categories:image-to-3d",
"language:en",
"language:code",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.24... | 2026-04-11T16:07:48 | null | null |
69f90ba344b269a11ae242a6 | jamiequint/sf_criminal_court | jamiequint | {"license": "cc-by-nc-4.0", "task_categories": ["tabular-classification"], "language": ["en"], "tags": ["criminal-justice", "san-francisco", "court-records", "district-attorney", "legal"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "cases", "data_files": [{"split": "train", "path": "cases.parquet"}]},... | false | False | 2026-05-04T23:34:38 | 26 | 26 | false | ae40a0c236db5ec90a034a1a7855a13607bd8b1d |
San Francisco Criminal Court Data
Linked criminal-court records for San Francisco County, combining scraped Superior Court docket data, District Attorney open-data feeds, and a charge-disposition spreadsheet that the Court produced only after sustained pressure under California Rules of Court rule 10.500.
... | 486 | 486 | 37,542,826 | [
"task_categories:tabular-classification",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
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"library:polars",
"library:mlcroissant",
"region:us",
"criminal-justice",
"san-francisco... | 2026-05-04T21:12:03 | null | null |
69e9d88e467b778820c1d942 | apptek-com/apptek_callcenter_dialogues | apptek-com | {"license": "cc-by-sa-4.0", "task_categories": ["automatic-speech-recognition"], "language": ["en"], "tags": ["audio", "automatic-speech-recognition", "speech", "conversational-speech", "long-form", "call-center", "multi-accent", "accent-robustness", "benchmark", "wer"], "pretty_name": "AppTek Call-Center Dialogues", "... | false | False | 2026-05-04T08:07:11 | 25 | 24 | false | 178fc976a809d6124b7c1fa59301a94b82b324ce |
AppTek Call-Center Dialogues: A Multi-Accent Long-Form Benchmark for English ASR
AppTek Call-Center Dialogues is a long-form conversational speech dataset for automatic speech recognition (ASR), featuring diverse English accents
across multiple service-oriented domains and designed to evaluate models on rea... | 2,644 | 2,644 | 34,852,499,968 | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:json",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.27543",
"region:us",
"audio",
"aut... | 2026-04-23T08:30:06 | 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 | 293 | 23 | 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... | 8,973 | 9,602 | 1,616,105,008 | [
"task_categories:text-generation",
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"license:apache-2.0",
"size_categories:10K<n<100K",
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"library:polars",
"library:mlcroissant",
"region:us",
"tool-calling",
"function-calling... | 2026-03-30T15:48:57 | 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 | 55 | 22 | 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... | 4,796 | 4,796 | 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 |
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 | 74 | 19 | 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... | 81,705 | 934,020 | 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 |
69f70f817613dfaddff80769 | iletisim/dezenformasyon-bultenleri | iletisim | {"language": ["tr", "en"], "license": "cc-by-4.0", "task_categories": ["text-classification", "question-answering"], "tags": ["dezenformasyon", "disinformation", "fact-checking", "claim-review", "turkish", "iletisim-baskanligi", "schema-org"], "pretty_name": "\u0130leti\u015fim Ba\u015fkanl\u0131\u011f\u0131 Dezenforma... | false | False | 2026-05-03T09:10:37 | 23 | 19 | false | 7e8c04bc6907873c6e3177b86da4f56cb782fab8 |
İletişim Başkanlığı Dezenformasyon Bültenleri
Kaynak API: llm.iletisim.gov.trKaynak Bültenler: iletisim.gov.tr/turkce/dezenformasyon-bulteni
T.C. Cumhurbaşkanlığı İletişim Başkanlığı Dezenformasyonla Mücadele Merkezi (DMM) tarafından haftalık yayımlanan Dezenformasyon Bültenleri'nin tüm iddia–gerçek kontrolü... | 163 | 163 | 1,154,787 | [
"task_categories:text-classification",
"task_categories:question-answering",
"source_datasets:original",
"language:tr",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"librar... | 2026-05-03T09:04:01 | 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 | 53 | 18 | 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... | 18,515 | 18,520 | 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 |
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 | 44 | 18 | 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... | 2,381 | 2,381 | 12,794,663,592 | [
"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 |
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 | 70 | 17 | 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... | 4,735 | 12,956 | 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 |
69f75f0cebff1de2d69553be | r0b0tlab/deepseek-hermes-reasoning-traces | r0b0tlab | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["hermes", "agent", "tool-calling", "reasoning", "sft", "lora", "function-calling", "deepseek", "chatml"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "tr... | false | False | 2026-05-04T00:18:27 | 17 | 17 | false | 28a1a874f9db2b8d41907a13f1c246ab6bba94f8 |
DeepSeek V4 Pro Hermes Reasoning Traces
19,331 multi-turn ChatML + Hermes reasoning traces generated by DeepSeek V4 Pro. Designed for LoRA fine-tuning local models to operate as Hermes Agent instances.
Quick Start
\
Splits
Split
Traces
train
16,431
valid
1,933
test
967
... | 550 | 550 | 77,262,957 | [
"task_categories:text-generation",
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"license:apache-2.0",
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"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"hermes",
"... | 2026-05-03T14:43:24 | null | null |
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 | 118 | 15 | 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... | 18,062 | 20,983 | 224,783,955 | [
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"language:en",
"language:code",
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"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",... | 2026-04-06T13:07:23 | 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 | 77 | 14 | 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... | 7,916 | 13,182 | 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 |
69dfd6eae232bc495ab58322 | OwnedByDanes/Usenet-Corpus-1980-2013 | OwnedByDanes | {"license": "other", "license_name": "custom-research-license", "license_link": "LICENSE", "task_categories": ["text-generation", "text-classification"], "language": ["en", "pl", "nl", "es", "fr", "it", "de", "ru", "ja", "pt"], "tags": ["usenet", "internet-history", "forums", "long-form-text", "pre-web", "conversationa... | false | False | 2026-05-05T16:17:39 | 14 | 14 | false | 6e644352cad17e0cba13983d7265d09bf54c31f4 |
Usenet Corpus 1980–2013
One of the largest curated Usenet archives in existence — 103.1 billion tokens of human discourse spanning three decades of internet history.
⚠️ Note on size indicator: The size badge above reflects the sample files hosted in this repository (65K rows). The full corpus is 103.1B toke... | 322 | 322 | 40,451,097 | [
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"language:pl",
"language:nl",
"language:es",
"language:fr",
"language:it",
"language:de",
"language:ru",
"language:ja",
"language:pt",
"license:other",
"size_categories:10K<n<100K",
"format:json",
... | 2026-04-15T18:20:26 | null | null |
69f386bce5f62b6293c48028 | sequelbox/Tachibana4-DeepSeek-V4-Pro-PREVIEW | sequelbox | {"license": "apache-2.0", "tags": ["tachibana", "tachibana-4", "early-preview", "agentic", "agentic-coding", "python", "c++", "c#", "c", "rust", "java", "javascript", "typescript", "go", "haskell", "shell", "r", "ruby", "algorithms", "data-structures", "concurrency", "api", "sql", "database", "auth", "ui", "mobile", "g... | false | False | 2026-04-30T17:12:47 | 16 | 13 | false | e73ef0809b3fbc8f3b3f781adb88e4b53be5a2cb | Click here to support our open-source dataset and model releases - help us speed up our release schedule!
This is an early sneak preview of Tachibana 4, containing the first 1.2k rows!
Tachibana 4 is an upcoming agentic coding dataset, generated by DeepSeek-V4-Pro:
Questions prioritize real-world, challenging agentic... | 206 | 206 | 62,680,057 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"tachibana",
"tachibana-4",
"early-preview",
"agentic",
"agenti... | 2026-04-30T16:43:40 | 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 | 1,061 | 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... | 536,493 | 6,965,085 | 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 |
69b3fa8c8dd0cb1205153394 | TAAC2026/data_sample_1000 | TAAC2026 | {"license": "cc-by-nc-4.0", "tags": ["TAAC2026", "recommendation"]} | false | False | 2026-04-10T09:07:28 | 86 | 12 | 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.... | 12,167 | 19,144 | 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 |
69f20d49db0ca6ec840c88e4 | unh1nge/comfyui-character-composer | unh1nge | {"license": "apache-2.0", "task_categories": ["image-to-image"], "language": ["en"], "tags": ["comfyui", "qwen", "character-generation", "custom-node", "workflow", "image-generation"], "pretty_name": "ComfyUI Character Composer", "version": 1.2, "release_date": "2026-04-30T00:00:00.000Z", "size_categories": ["n<1K"]} | false | False | 2026-05-07T19:19:01 | 13 | 12 | false | 798f7e4b452ccd22a7fba585530eda2743d13caf |
AIO Qwen Workflow
The repository now includes:
AIO Comfyui-Character-Composer Qwen Workflow.json
A unified all-in-one Qwen workflow designed around Character Composer.
This is no longer just:
a custom node
a tag helper
a prompt formatter
It is effectively a lightweight procedural character-gen... | 4,919 | 4,919 | 9,371,293 | [
"task_categories:image-to-image",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"comfyui",
"qwen",
"character-generation",
"custom-node",
"workflow",
"image-generation"
] | 2026-04-29T13:53:13 | null | null |
69f2f889ada285df7c42f635 | microsoft/synthetic-computers-at-scale | microsoft | {"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["other"], "tags": ["synthetic", "computer-use", "agents", "persona", "filesystem"], "pretty_name": "Synthetic Computers", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}], "de... | false | False | 2026-05-01T06:30:14 | 12 | 11 | false | 40e780a399dc0426516dd4007c56ca3ff06db36f |
Synthetic Computers
Paper: Synthetic Computers at Scale for Long-Horizon Productivity Simulation (arXiv:2604.28181)
A dataset of 98 synthetic computer environments designed for research on
computer-use agents, long-horizon planning, and persona-grounded reasoning.
Each row describes a single fictional user's... | 1,156 | 1,156 | 1,505,075,125 | [
"task_categories:other",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
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"library:polars",
"library:mlcroissant",
"arxiv:2604.28181",
"region:us",
"synthetic",
"computer-use",
"agents",
"persona",
"filesys... | 2026-04-30T06:36:57 | null | null |
69f4a6c4b9ad68723be0a884 | clem/ml-intern-sessions | clem | {"pretty_name": "ML Intern Session Traces", "language": ["en"], "license": "other", "task_categories": ["text-generation"], "tags": ["agent-traces", "coding-agent", "ml-intern", "session-traces", "claude-code", "hf-agent-trace-viewer"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "s... | false | False | 2026-05-05T18:26:42 | 12 | 11 | false | 28f2d01794ca9e22f5a9649dfdd8d358d29171ce |
ML Intern session traces
This dataset contains ML Intern coding agent session traces uploaded from local
ML Intern runs. The traces are stored as JSON Lines files under sessions/,
with one file per session.
Links
ML Intern demo: https://smolagents-ml-intern.hf.space
ML Intern CLI: https://github.... | 1,457 | 1,457 | 5,745,919 | [
"task_categories:text-generation",
"language:en",
"license:other",
"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",
"coding-agent",
... | 2026-05-01T13:12:36 | null | null |
69fa86fc707a940fd2ae2e3c | dianetc/OBLIQ-Bench | dianetc | {"license": "cc-by-4.0", "language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-retrieval"], "tags": ["retrieval", "reasoning", "benchmark", "oblique-queries"], "configs": [{"config_name": "math", "data_files": [{"split": "corpus", "path": "analogues/math/corpus/corpus.jsonl"}, {"split": "queri... | false | False | 2026-05-08T13:06:56 | 10 | 10 | false | d48a6402abda7b265d7fb49fd5835006a4d67da4 |
OBLIQ-Bench
Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries
OBLIQ-Bench is a suite of five retrieval benchmarks designed to expose a blind spot in modern search systems: oblique queries, where the attributes that determine relevance are latent and have little or no surfa... | 134 | 134 | 2,922,101,400 | [
"task_categories:text-retrieval",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"retrieval",
"reasoning",
"benchmark",
"oblique-queries"
] | 2026-05-06T00:10:36 | null | null |
69fa94c2dae9b5bbe9c86e84 | Inferact/codex_swebenchpro_traces | Inferact | {"license": "mit"} | false | False | 2026-05-07T01:13:21 | 11 | 10 | false | 0d52ae8c75738117be9e58c7071bd9a5b43ff78f | This is a dataset generated by real swebenchpro agentic workload trace + codex agent.
1. Eval Result Summary
Metric
Value
Total trials
731
Successful trials
610
Failed trials
120
No data (skipped)
1
Passed
329
Pass rate (of successful)
53.9%
Per-Repo Breakdown
... | 55 | 55 | 218,659,022 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-05-06T01:09:22 | 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 | 1,296 | 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... | 917,018 | 11,258,503 | 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 |
65dc13085ca10be41fdd8b27 | bigcode/the-stack-v2 | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack-v2", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_ga... | false | auto | 2024-04-23T15:52:32 | 553 | 9 | false | 7408bfbcfd48e5833d62fd3dba48afd20d109473 |
The Stack v2
The dataset consists of 4 versions:
bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here
bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated
bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but... | 19,490 | 286,465 | 839,609,928,672 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
... | 2024-02-26T04:26:48 | null | null |
69f07b073421d649e7c61ecd | beyoru/Aesir-Character-CoT-roleplay | beyoru | {"license": "apache-2.0", "language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "Aesir Character CoT Roleplay", "tags": ["roleplay", "chain-of-thought", "character-ai", "distilled", "creative-writing", "sft", "reasoning-effort-max", "deepseek-ai", "sft", "reasoning-... | false | False | 2026-05-03T12:59:09 | 11 | 9 | false | 04c001e431342bee843ed476f1abf2e4ebd4b46b |
Overview
Think with your role.
Most reasoning datasets teach models to think like an AI. This one teaches them to think like the character.
Continue updating until money run out, I will try to update this dataset in near future
Stats
1,973 high-quality conversations (filtered from 2,000... | 551 | 551 | 28,677,582 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"roleplay",
"... | 2026-04-28T09:16:55 | null | null |
69e4aa7ea8ad7ec14c63ae71 | Roman1111111/claude-sonnet-4.6-120000x | Roman1111111 | null | false | False | 2026-04-19T10:59:32 | 68 | 8 | 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... | 5,180 | 5,180 | 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 |
69e7c30f4bccf73cfe458752 | openai/healthbench-professional | openai | {"license": "mit", "tags": ["health", "healthbench"], "pretty_name": "HealthBench Professional"} | false | False | 2026-04-22T16:09:30 | 51 | 8 | 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... | 8,142 | 8,142 | 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 |
69efdf6acfcae9ec6771f191 | PleIAs/CommonLingua-Train | PleIAs | {"license": "other", "license_name": "per-source", "language": ["multilingual"], "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "tags": ["language-identification", "common-corpus", "african-languages"], "pretty_name": "CommonLingua-Train"} | false | False | 2026-04-28T08:53:16 | 15 | 8 | false | a452a0816459c4370f1e440b5341ad82b14c583f |
CommonLingua-Train
This is the training dataset for PleIAs/CommonLingua — a byte-level language identification model for 334 languages. It is composed of 2.48 M paragraphs, sourced exclusively from Wikipedia and other open-licensed and public-domain corpora extracted from Common Corpus.
The training dataset ... | 221 | 221 | 1,195,027,297 | [
"task_categories:text-classification",
"language:multilingual",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"language-identification",
"common-corpus",
"african-l... | 2026-04-27T22:12:58 | null | null |
69f3819fa2c4fd1b1b1c65b1 | NodeLinker/deepseek-ai-Thinking-with-Visual-Primitives-deleted-repo | NodeLinker | {"license": "mit", "viewer": false} | false | False | 2026-05-01T19:27:38 | 12 | 8 | false | eacaa5e8c89abab17f84c523d8efa03de4713dd5 |
Thinking with Visual Primitives
English |
简体中文
News
2026.04.30: We have released the technical report detailing our approach. In the near future, we plan to make the in-house benchmarks and a subset of our cold-start data publicly available. The model weights... | 7,838 | 7,838 | 14,283,512 | [
"license:mit",
"region:us"
] | 2026-04-30T16:21:51 | null | null |
69f67f945ec43b12d4e0d1cd | KIT-MRT/KITScenes-Multimodal | KIT-MRT | {"pretty_name": "KITScenes Multimodal", "license": "cc-by-nc-4.0", "language": ["en"], "annotations_creators": ["expert-generated"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "tags": ["autonomous-driving", "multimodal", "robotics", "computer-vision", "lidar", "radar", "hd-maps", "lanelet2"], "ga... | false | auto | 2026-05-06T18:09:23 | 8 | 8 | false | 104479b84a89fd4e264800021bac26a6b4cd15b8 |
The KITScenes Multimodal Dataset
Early pre-release. This is an early pre-release version of KITScenes Multimodal and may contain minor labeling errors and format changes. We recommend waiting for the upcoming full release if dataset stability is important for your use case.
KITScenes Multimodal is a high-... | 271 | 271 | 4,910,955,363,016 | [
"annotations_creators:expert-generated",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"region:us",
"autonomous-driving",
"multimodal",
"robotics",
"computer-vision",
"lidar",
"radar",
"hd-maps",
"lanelet2"
] | 2026-05-02T22:49:56 | null | null |
69fbb6f7668a521b200b0ec6 | sequelbox/Tachibana4-DeepSeek-V4-Pro | sequelbox | {"license": "apache-2.0", "tags": ["tachibana", "tachibana-4", "agentic", "agentic-coding", "python", "c++", "c#", "c", "rust", "java", "javascript", "typescript", "go", "haskell", "shell", "r", "ruby", "algorithms", "data-structures", "concurrency", "api", "sql", "database", "auth", "ui", "mobile", "gamedev", "physics... | false | False | 2026-05-07T01:09:32 | 8 | 8 | false | 80304ea7aaa9dff66d3b674702d9534da7bdc7fe | Click here to support our open-source dataset and model releases - help us speed up our release schedule!
Tachibana 4 is an agentic coding dataset, testing the limits of DeepSeek-V4-Pro's coding skills:
Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topi... | 24 | 24 | 910,052,982 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"doi:10.57967/hf/8696",
"region:us",
"tachibana",
"tachibana-4",
"agentic",
... | 2026-05-06T21:47:35 | null | null |
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 | 306 | 7 | 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... | 13,044 | 125,623 | 2,689,226,423 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
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"library:dask",
"library:mlcroissant",
"library:polars",
"library:datadesigner",
"region:us",
"synthetic",
"personas",
"NVIDIA",
"da... | 2025-06-09T04:12:11 | null | null |
69eae8d5541105e37c7f0af5 | beyoru/Deepseek-v4-pro-max-distill-1500x | beyoru | {"license": "apache-2.0", "language": ["en"], "task_categories": ["text-generation"], "tags": ["reasoning", "distillation", "chain-of-thought", "deepseek", "deepseek-v4-pro", "math"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train.parquet"},... | false | False | 2026-05-04T11:28:06 | 23 | 7 | false | 9d3af0fa5c488f6c5cb7a5644dadb792ad084d4c |
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... | 1,307 | 1,307 | 43,318,643 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"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 |
69f417f829066501456fdab6 | ChrisHayduk/nanofold-public | ChrisHayduk | {"pretty_name": "NanoFold Public", "license": "cc-by-4.0", "task_categories": ["feature-extraction"], "tags": ["protein-folding", "protein-structure", "structural-biology", "openproteinset", "openfold", "nanofold"], "size_categories": ["10K<n<100K"]} | false | False | 2026-05-01T04:11:19 | 7 | 7 | false | 0a580ad87b2b19fb6772d40fddb83605d78a6a6c |
NanoFold Public
NanoFold Public is the public train/validation portion of the nanoFold protein-folding benchmark. It packages a compact, fixed, auditable subset of OpenProteinSet/OpenFold-derived protein structure training data for fast iteration on data-efficient folding models.
The dataset has 10000 train ... | 991 | 991 | 1,093,060,699 | [
"task_categories:feature-extraction",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2308.05326",
"region:us",
"protein... | 2026-05-01T03:03:20 | 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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