datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.BeyondSWE-harbor
BeyondSWE-harbor
This repository provides the harbor version of the BeyondSWE benchmark, containing the full task instances in a directory-based (harbor) format, where each instance is stored as an independent folder.
📌 For benchmark definition, data format and detailed evaluation results, please refer to: 🤗 Main Dataset on HuggingFace
🗂️ Data Structure
beyondswe/
├── {instance_id}/
│ ├── environment/
│ ├── solution/
│ ├── tests/
│ ├──… See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/BeyondSWE-harbor.unclickbait-synthetic-27b-trajectories
Unclickbait Synthetic 27B Trajectories
Synthetic trajectory dataset generated by using rich structured JSON prompts and validated by two-stage judging pipeline.
Contents
: Full generated trajectories (current snapshot: 48,623 records out of 152,369 pristine event candidates).
: 30 benchmark test samples audited end-to-end through the 122B two-stage judge (Stage 1 integrity gate + Stage 2 4D scoring).
AM-DeepSeek-Distilled-40MFor more open-source datasets, models, and methodologies, please visit our GitHub repository and paper: DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training.
Due to certain constraints, we are only able to open-source a subset of the complete dataset.
Model Training Performance based on our complete dataset
On AIME 2024, our 72B model achieved a score of 79.2 using only supervised fine-tuning (SFT). The 32B model reached 75.8 and… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-Distilled-40M.BeyondSWE
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
BeyondSWE is a comprehensive benchmark that evaluates code agents along two key dimensions — resolution scope and knowledge scope — moving beyond single-repo bug fixing into the real-world deep waters of software engineering.
✨ Highlights
500 real-world instances across 246 GitHub repositories, spanning four distinct task settings
Two-dimensional evaluation: simultaneously… See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/BeyondSWE.YuLan-Mini-Text-Datasets
News
[2025.04.11] Add dataset mixture: link.
[2025.03.30] Text datasets upload finished.
This is text dataset.
这是文本格式的数据集。
Since we have used BPE-Dropout, in order to ensure accuracy, you can find the tokenized dataset here.
由于我们使用了BPE-Dropout，为了保证准确性，你可以在这里找到分词后的数据。
For more information, please refer to our datasets details and preprocess details.
Contributing
We welcome any form of contribution, including feedback on model bad cases, feature suggestions, and example… See the full description on the dataset page: https://huggingface.co/datasets/yulan-team/YuLan-Mini-Text-Datasets.moss-002-sft-data
Dataset Card for "moss-002-sft-data"
Dataset Summary
An open-source conversational dataset that was used to train MOSS-002. The user prompts are extended based on a small set of human-written seed prompts in a way similar to Self-Instruct. The AI responses are generated using text-davinci-003. The user prompts of en_harmlessness are from Anthropic red teaming data.
Data Splits
name
# samples
en_helpfulness.json
419049
en_honesty.json
112580… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/moss-002-sft-data.oercommons-v1-optimized
OERCommons v1 Optimized
Authors: Junjie Wang and Yuhan SunHosted by: PIN TeamDataset: pin-team/oercommons-v1-optimized
OERCommons v1 Optimized is a provenance-preserving multimodal pretraining corpus built on the OERCommons subset of The Common Pile v0.1, which serves as its upstream data and licensing baseline. We extend it with full-page recovery, canonical Markdown, ordered image/PDF/link metadata, conservative corrections, and integrity evidence.
At a glance… See the full description on the dataset page: https://huggingface.co/datasets/pin-team/oercommons-v1-optimized.Ultimate-Offensive-Red-Team
Ultimate Red Team AI Training Dataset 💀
Dataset Description
A comprehensive dataset for training AI models in offensive security, red team operations, and penetration testing. This dataset combines real-world vulnerability data, exploitation techniques, and operational frameworks to create an AI capable of autonomous red team operations.
Dataset Summary
Total Data Points: 550,000+ unique security-related entries
Categories: 15+ major security domains… See the full description on the dataset page: https://huggingface.co/datasets/WNT3D/Ultimate-Offensive-Red-Team.bactrainus-hotpotqa-teacher-traces
Bactrainus HotpotQA Teacher Traces
SOURCE-LINKED v1.0.0
Archived Llama 3.1 rationale and question-decomposition supervision, paired with complete SFT conversations and stable HotpotQA identities.
198,660 ROWS
4 CONFIGURATIONS
SFT MESSAGES
8B + 70B LABELS
CC BY-SA 4.0
A focused release of recovered teacher-generated supervision for multi-hop question answering. Every row contains the normalized annotation, an ordered… See the full description on the dataset page: https://huggingface.co/datasets/bactrianus/bactrainus-hotpotqa-teacher-traces.teambench
TeamBench: Evaluating Agent Coordination under Enforced Role Separation
Overview
TeamBench is a benchmark of 851 task templates that expand to 931 seeded evaluation instances across 19 base categories (the leaderboard uses 21 refined categories; see paper §3.1). It evaluates whether LLM-based agent teams outperform a single oracle agent under OS-enforced role separation (Planner / Executor / Verifier in isolated sandboxes with distinct tool allow-lists), and… See the full description on the dataset page: https://huggingface.co/datasets/ybkim95/teambench.agentic_red_team
Agentic Red Team Tool-Calling Dataset
A multi-turn, tool-calling cybersecurity dataset where each example is a complete agentic trajectory — a realistic sequence of tool calls, tool responses, and reasoning steps that an AI agent would execute during an authorized red team engagement.
Overview
This dataset contains 5,000 agentic tool-calling examples across 20 offensive security sectors. Unlike traditional Q&A datasets, each row is a complete multi-turn trajectory… See the full description on the dataset page: https://huggingface.co/datasets/sh111111111111111/agentic_red_team.pbt-bench
PBT-Bench: Benchmarking AI Agents on Property-Based Testing
PBT-Bench is a benchmark of 100 curated property-based testing problems across 40 Python libraries, accompanied by 4,800 evaluation trajectories from 8 contemporary LLMs.
Benchmark Overview
Each problem injects one or more semantic bugs into a real Python library. The bugs violate documented semantic invariants and are designed to be reliably detectable through Hypothesis @given property tests with carefully… See the full description on the dataset page: https://huggingface.co/datasets/pbtbench-team/pbt-bench.red_team_repo_social_bias_prompts
Dataset Card for A Red-Teaming Repository of Existing Social Bias Prompts
Summary
This dataset contains aggregated and unified existing red-teaming prompts designed to identify
stereotypes, discrimination, hate speech, and other representation harms in text-based Large Language Models (LLMs)
Project Summary Page: For more information about my 2024 AI Safety Capstone project
Dataset Information: For more information about the datasets used to create this repository.… See the full description on the dataset page: https://huggingface.co/datasets/svannie678/red_team_repo_social_bias_prompts.DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x
DeepSeek V4 Flash 0731 Teacher Distillation — 40,513 Retained Rows
Teacher-distillation corpus generated with
deepseek-ai/DeepSeek-V4-Flash-0731.
The original manifest contained 45,000 unique seeds.
Following generation, QC, retry-based repair, quarantine auditing,
and recovery adjudication, 40,513 rows were retained.
Composition
Bucket
Rows
Coding
5,601
Agentic
9,982
Cyber blue
13,000
Controlled cyber red
6,999
Tool use
4,931
Total
40,513… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x.omnimcp_enterprise_dataops_lakehouse_village_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_enterprise_dataops_lakehouse_village_teaser.omnimcp_cyber_siem_triage_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cyber_siem_triage_teaser.AM-Math-Difficulty-RLFor more open-source datasets, models, and methodologies, please visit our GitHub repository.
We believe that the selection of training data for reinforcement learning is crucial.
To validate this, we conducted several experiments exploring how data difficulty influences training performance.
Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL.omnimcp_cyber_token_revocation_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_cyber_token_revocation_teaser.teasecorpus
teasecorpus
A Chinese ChatML SFT dataset generated from the 「擅长捉弄的高木同学」Fandom wiki, with record-level contributor provenance tracked by originblame.
Summary: 1,410 question-answer pairs in ChatML format, covering 7 content types (chapters, characters, episodes, music, volumes, seasons, movies). Each record is traceable to its source wiki page and all contributors who edited that page, via originblame — a record-level provenance system. If a contributor requests content removal… See the full description on the dataset page: https://huggingface.co/datasets/tzbkk/teasecorpus.Ultimate-Offensive-Red-Team
Ultimate Red Team AI Training Dataset 💀
Dataset Description
A comprehensive dataset for training AI models in offensive security, red team operations, and penetration testing. This dataset combines real-world vulnerability data, exploitation techniques, and operational frameworks to create an AI capable of autonomous red team operations.
Dataset Summary
Total Data Points: 550,000+ unique security-related entries
Categories: 15+ major security domains… See the full description on the dataset page: https://huggingface.co/datasets/Cyberpluis/Ultimate-Offensive-Red-Team.hle-extract-qwen3235ba22b-20250815
HLE Extract: Qwen3-235B-A22B Evaluation Results (2025-08-15)
Dataset Description
This dataset contains the complete Human-Level Evaluation (HLE) benchmark with detailed evaluation results from the Qwen/Qwen3-235B-A22B model. It merges the original team-suzuki/hle-extract dataset with comprehensive model responses and human judgments.
Dataset Summary
Total Questions: 120 (complete HLE dataset)
Evaluated Questions: 103 (85.8%)
Unevaluated Questions: 17 (14.2%)… See the full description on the dataset page: https://huggingface.co/datasets/team-suzuki/hle-extract-qwen3235ba22b-20250815.guidelinesThis is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/epfl-llm/guidelines
🎉 NEW DROP 🎉 PubMed Guidelines
We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!
Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online… See the full description on the dataset page: https://huggingface.co/datasets/aisc-team-a1/guidelines.guidelinesThis is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/epfl-llm/guidelines
🎉 NEW DROP 🎉 PubMed Guidelines
We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!
Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online… See the full description on the dataset page: https://huggingface.co/datasets/aisc-team-b1/guidelines.dflash-code-multilingual-teacher-responses-qwen235b
Code + Multilingual Teacher Responses (Qwen3-235B-A22B-Instruct-2507)
This repo now contains 302,800 total samples across the main blended
data.jsonl / .parquet file plus a second Nemotron-only file
(nemotron_code_teacher_responses.jsonl / .parquet). All responses were
generated by Qwen3-235B-A22B-Instruct-2507 in non-thinking mode
(enable_thinking=false) to match downstream speculator training and eval.
Built in two batches: an initial 59,506-row batch (50K code + 9.5K… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/dflash-code-multilingual-teacher-responses-qwen235b.Ultimate-Offensive-Red-Team
Ultimate Red Team AI Training Dataset 💀
Dataset Description
A comprehensive dataset for training AI models in offensive security, red team operations, and penetration testing. This dataset combines real-world vulnerability data, exploitation techniques, and operational frameworks to create an AI capable of autonomous red team operations.
Dataset Summary
Total Data Points: 550,000+ unique security-related entries
Categories: 15+ major security domains… See the full description on the dataset page: https://huggingface.co/datasets/Korzo/Ultimate-Offensive-Red-Team.red_team
Red Team Dataset
A structured cybersecurity dataset where each example is a complete reasoning trajectory — a realistic sequence of steps and explanations that an AI assistant would produce during an authorized red team engagement.
Overview
This dataset contains 8,889 red team examples across 26 offensive security topics. Unlike traditional Q&A datasets, each row is a complete reasoning trajectory where an AI assistant plans an attack, explains methodology, reasons… See the full description on the dataset page: https://huggingface.co/datasets/sh111111111111111/red_team.omnimcp_graphrag_knowledge_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_graphrag_knowledge_teaser.TeachArena
TeachArena
TeachArena is a benchmark for evaluating AI tutoring agents across the full teaching
decision chain — from moment-to-moment tutoring dialogue, to pedagogical judgment on
packaged evidence, to multi-step teaching workflows grounded in a learning-management
system. It contains 354 tasks organized into three stages, a mock LMS environment
database, the agent policy documents, and the full scoring logic.
Why three stages
A capable teaching agent must both… See the full description on the dataset page: https://huggingface.co/datasets/CinderD/TeachArena.whittle-teacher32-complete-answers
Whittle teacher32: complete answers with per-token teacher logprobs
Research preview. Part of the Whittle compression campaign, a personal
research project. The compute for this project is self funded and donations
decide whether the next round happens: https://ko-fi.com/davida81328
What this is
Complete answers generated by Qwen3.8-27B (UD-Q5_K_XL via llama.cpp), each
ending on a real end-of-turn token because the answer is finished, with the
teacher's top-32… See the full description on the dataset page: https://huggingface.co/datasets/logic65/whittle-teacher32-complete-answers.