datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hermes-function-calling-v1
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.databricks-dolly-15k
Summary
databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several
of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification,
closed QA, generation, information extraction, open QA, and summarization.
This dataset can be used for any purpose, whether academic or commercial, under the terms of the
Creative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k.reddit_dataset_157
Bittensor Subnet 13 Reddit Dataset
Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more information about the dataset, please visit the official repository.
Supported Tasks
The versatility of this dataset allows… See the full description on the dataset page: https://huggingface.co/datasets/tensorshield/reddit_dataset_157.python_code_instructions_18k_alpaca
Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the source here.
Terminal-Lego-15k
[!NOTE]
This private PrimeIntellect mirror is filtered from
SWE-Lego/Terminal-Lego-15k.
It reflects ad6e280 from prime-data PR #34: 1,224
tasks are excluded and 13,825 task directories remain.
Each retained task's task.toml sets [environment].docker_image to its
prebuilt Prime platform image in the canonical org-less form
terminal-lego/<task-id>:latest (2026-07 registry homogenization; the images
were previously team-scoped as team-…/terminal-lego:<task-id>-<build-hash>).
The root… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Terminal-Lego-15k.ZGCM-1-Data
A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
Zhongguancun Academy · Zhongguancun Institute of Artificial Intelligence
📄 Tech Report · 🤗 Model · 🤗 Data · 📊 Results · 💻 Training Code · 💬 WeChat Community
Introduction
ZGCM-1 is a 7.39B-parameter dense language model trained from scratch, built for mathematical reasoning and tool-assisted search. It combines deliberate internal thinking with active information gathering, supporting… See the full description on the dataset page: https://huggingface.co/datasets/zgcagi/ZGCM-1-Data.WildChat-1M
Dataset Card for WildChat
Dataset Description
Paper: https://arxiv.org/abs/2405.01470
Interactive Search Tool: https://wildvisualizer.com (paper)
License: ODC-BY
Language(s) (NLP): multi-lingual
Point of Contact: Yuntian Deng
Dataset Summary
WildChat is a collection of 1 million conversations between human users and ChatGPT, alongside demographic data, including state, country, hashed IP addresses, and request headers. We collected WildChat by… See the full description on the dataset page: https://huggingface.co/datasets/allenai/WildChat-1M.ChatGPT-Jailbreak-Prompts
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
medical-o1-reasoning-SFT
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.Cambrian-10M
Cambrian-10M Dataset
Please see paper & website for more information:
https://cambrian-mllm.github.io/
https://arxiv.org/abs/2406.16860
Overview
Cambrian-10M is a comprehensive dataset designed for instruction tuning, particularly in multimodal settings involving visual interaction data. The dataset is crafted to address the scarcity of high-quality multimodal instruction-tuning data and to maintain the language abilities of multimodal large language models (LLMs).… See the full description on the dataset page: https://huggingface.co/datasets/nyu-visionx/Cambrian-10M.SciCodeThis dataset was presented in SciCode: A Research Coding Benchmark Curated by Scientists.
KodCode-V1-SFT-R1
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1.AIR-Bench-Dataset
AIR-Bench
Arxiv: https://arxiv.org/html/2402.07729v1This is the AIR-Bench dataset download page.AIR-Bench encompasses two dimensions: foundation and chat benchmarks.
The former consists of 19 tasks with approximately 19k single-choice questions.
The latter one contains 2k instances of open-ended question-and-answer data.For how to run AIR-Bench, Please refer to AIR-Bench github page(https://github.com/OFA-Sys/AIR-Bench)(will be public soon).
Data Sources(All come from… See the full description on the dataset page: https://huggingface.co/datasets/qyang1021/AIR-Bench-Dataset.Gaokao-Compass-11M
English
GaokaoCompass — China College Admission Dataset
GaokaoCompass is a structured dataset of China's national college entrance examination (Gaokao) admission records, covering all 31 provinces from 2017 to 2025. It includes enrollment plans, university admission cutoff scores, major-level admission scores, and score-ranking tables. The dataset is designed to help students, parents, and researchers make informed decisions with… See the full description on the dataset page: https://huggingface.co/datasets/choucsan/Gaokao-Compass-11M.OpenMathInstruct-1
OpenMathInstruct-1
OpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed Mixtral-8x7B model.
The problems are from GSM8K
and MATH training subsets and the solutions
are synthetically generated by allowing Mixtral model to use a mix of text reasoning and
code blocks executed by Python interpreter.
The dataset is split into train and validation subsets that we used in the ablations experiments.
These two subsets… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-1.NuminaMath-1.5-RL-Verifiable
Dataset Card for NuminaMath-1.5-RL-Verifiable
Dataset Summary
NuminaMath-1.5-RL-Verifiable is a curated subset of the NuminaMath-1.5 dataset, specifically filtered to support reinforcement learning applications requiring verifiable outcomes. This collection consists of 131,063 math word problems from the original dataset that meet strict filtering criteria: all problems have definitive numerical answers, validated problem statements and solutions, and come from… See the full description on the dataset page: https://huggingface.co/datasets/nlile/NuminaMath-1.5-RL-Verifiable.STARK_10k
STARK: Spatial-Temporal reAsoning benchmaRK
STARK is a comprehensive benchmark designed to systematically evaluate large language models (LLMs) and large reasoning models (LRMs) on spatial-temporal reasoning tasks, particularly for applications in cyber-physical systems (CPS) such as robotics, autonomous vehicles, and smart city infrastructure.
Dataset Summary
Hierarchical Benchmark: Tasks are structured across three levels of reasoning complexity:
State Estimation:… See the full description on the dataset page: https://huggingface.co/datasets/prquan/STARK_10k.Belle_1.4M-SLAM-Omni
Belle_1.4M
This dataset is prepared for the reproduction of SLAM-Omni.
This is a multi-round Chinese spoken dialogue training dataset. For code and usage examples, please refer to the related GitHub repository: X-LANCE/SLAM-LLM (examples/s2s)
🔧 Modifications
Data Filtering: We removed samples with excessively long data.
Speech Response Tokens: We used CosyVoice to synthesize corresponding semantic speech tokens for the speech response. These tokens, represented as… See the full description on the dataset page: https://huggingface.co/datasets/worstchan/Belle_1.4M-SLAM-Omni.SWE-Bench-Verified-O1-reasoning-high-results
SWE-Bench Verified O1 Dataset
Executive Summary
This repository contains verified reasoning traces from the O1 model evaluating software engineering tasks. Using OpenHands + CodeAct v2.2, we tested O1's bug-fixing capabilities on the SWE-Bench Verified dataset, achieving a 28.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code generation through enhanced action-based reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-reasoning-high-results.LongAlign-10k
LongAlign-10k
🤗 [LongAlign Dataset] • 💻 [Github Repo] • 📃 [LongAlign Paper]
LongAlign is the first full recipe for LLM alignment on long context. We propose the LongAlign-10k dataset, containing 10,000 long instruction data of 8k-64k in length. We investigate on trianing strategies, namely packing (with loss weighting) and sorted batching, which are all implemented in our code. For real-world long context evaluation, we introduce LongBench-Chat that evaluate the… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/LongAlign-10k.EMMOE-100
EMMOE-100 Trainset
Resources
Project
Paper
Code
Model
Dataset
Dataset Feature
Task Attributes
Task Example
Dataset Structure
EMMOE-100/
├── README.md
├── assets/
├── data/
│ └── train/
│ ├── 1/
│ │ ├── info.txt
│ │ ├── info_re1.txt
│ │ ├── info_re2.txt
│ │ ├── info_re3.txt
│ │ ├── keypath.json
│ │ ├── scene.json
│ │ ├──… See the full description on the dataset page: https://huggingface.co/datasets/Dongping-Li/EMMOE-100.taxbench-au
TaxBench-AU
A benchmark for testing whether AI agents can calculate Australian tax.
TaxBench-AU contains 156 Australian tax calculation questions, presented as multiple-choice (4-option) worked tax problems. The benchmark is designed to test whether an AI agent can read the facts, apply the right Australian tax rule for the relevant income year, do the calculation, and choose the correct answer.
The Kaggle mirror is published as Agent Tax Exam for Australian Tax.
Paper:… See the full description on the dataset page: https://huggingface.co/datasets/Pn101/taxbench-au.KodCode-Light-RL-10K
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-Light-RL-10K.Nemotron-AIQ-Agentic-Safety-Dataset-1.0
Nemotron-AIQ Agentic Safety Dataset
Dataset Summary
Nemotron-AIQ-Agentic-Safety-Dataset is a comprehensive dataset that captures a broad range of novel safety and security contextual risks that can emerge within agentic systems. It highlights the robustness of NVIDIA's open model, llama-3.3-nemotron-super-49b-v1, when deployed as a research assistant inside AIQ, demonstrating its ability to handle a diverse spectrum of agentic safety and security challenges. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-AIQ-Agentic-Safety-Dataset-1.0.LLaVA-Instruct-150K
LLaVA Visual Instruct 150K Dataset Card
Dataset details
Dataset type:
LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data.
It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability.
Dataset date:
LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API.
Paper or resources for more information:
https://llava-vl.github.io/
License:… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K.image2struct-latex-v1
Image2Struct - Latex
Paper | Website | Datasets (Webpages, Latex, Music sheets) | Leaderboard | HELM repo | Image2Struct repo
License: Apache License Version 2.0, January 2004
Dataset description
Image2struct is a benchmark for evaluating vision-language models in practical tasks of extracting structured information from images.
This subdataset focuses on LaTeX code. The model is given an image of the expected output with the prompt:
Please provide the LaTex code used to… See the full description on the dataset page: https://huggingface.co/datasets/stanford-crfm/image2struct-latex-v1.MAmmoTH-VL-Instruct-12M
MAmmoTH-VL-Instruct-12M
🏠 Homepage | 🤖 MAmmoTH-VL-8B | 💻 Code | 📄 Arxiv | 📕 PDF | 🖥️ Demo
Introduction
Our simple yet scalable visual instruction data rewriting pipeline consists of three steps: manual data source collection, rewriting using MLLMs/LLMs, and filtering via the same MLLM as a judge. Examples below illustrate transformations in math and science categories, showcasing detailed, step-by-step responses.
The data distribution of… See the full description on the dataset page: https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M.GameQA-140K
[ICLR 2026] Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
🎊 News
[2026/07] 🔥Peking University and Kuaishou Kling Team evaluate their agentic visual reasoning method Beacon on our GameQA benchmark. Beacon learns when tools are truly needed (Mode Adaptiveness) and how tool use extends capability on hard problems (Tool Effect), and achieves the highest accuracy on GameQA among open-source models of the same scale… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/GameQA-140K.PersonaMem-v1🚨 We have now released PersonaMem-v3 and PersonaMem-v2.
This is the official Huggingface repository of the paper Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale and the PersonaMem benchmark.
We present PersonaMem, a new LLM personalization benchmark to assess how well language models can infer evolving user profiles and generate personalized responses across task scenarios. PersonaMem emphasizes persona-oriented, multi-session… See the full description on the dataset page: https://huggingface.co/datasets/bowen-upenn/PersonaMem-v1.jee-neet-benchmark
JEE/NEET LLM Benchmark Dataset
🏆 View the live leaderboard → — interactive results across JEE Advanced, JEE Main & NEET, with open/closed-weight badges, contamination flags, and per-run cost.
A benchmark for evaluating vision-capable LLMs on Indian competitive exam questions (JEE Advanced & NEET). Each question is the original exam image; models answer via the OpenRouter API and are scored with authentic, exam-specific marking schemes — including partial credit for JEE… See the full description on the dataset page: https://huggingface.co/datasets/Reja1/jee-neet-benchmark.