Omni-Edu — instruction-tuning mixture

69,999 supervised instruction examples (~158M characters) covering K-12 subject competence, curriculum grounding, diagnostic reasoning, pedagogical action and general-purpose instruction. 12,146 rows (17.4%) are multimodal; every image referenced by the JSONL ships in this repository under images/.

Composition

Capability family	Examples
Subject competence	31,855
Pedagogical action and scaffolding	14,226
Curriculum grounding	9,381
General-purpose instruction	9,048
Diagnostic reasoning	5,489
Total	69,999

Usage

Load the released instruction mixture from Hugging Face:

pip install -U datasets

from datasets import load_dataset

train = load_dataset(
    "OmniEdu/Omni-Edu",
    "OmniEdu",
    split="train",
)
print(train)

The dataset covers curriculum grounding, problem solving, diagnosis, tutoring, and general instruction. Please follow the licenses and usage terms of the component datasets and source materials.

Format

JSON Lines, ShareGPT style, exactly three top-level keys per row:

{
  "messages": [
    {"role": "system",    "content": "..."},
    {"role": "user",      "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "images": ["..."],
  "extra_info": "{...}"
}

Citation

@article{liang2026omniedu,
  title={OmniEdu: Open Foundation Models for Learning and Teaching},
  author={Liang, Hao and Lin, Qihan and Qiang, Meiyi and Sun, Linzhuang and Feng, Hengyi and Chen, Mingrui and Qiu, Sizhe and Zhang, Wentao},
  journal={arXiv preprint arXiv:2609.23088},
  year={2026}
}