Dango-A1-A2 🍡

Dango-A1-A2 is a large language model supervised fine-tuned (SFT) on second-language (L2) learning lessons (English) covering levels A1 and A2.

It is further trained from Dango-pretrained, a Japanese-only model pretrained on 100 billion tokens from an extensively filtered Japanese corpus.

The model is intended primarily for research on second language acquisition and can also be used as a Japanese-English speaker simulator.

This repository contains the checkpoint after supervised fine-tuning on the A1–A2 L2 learning lessons.

Model Details

• Base model: Dango-pretrained (dango-1.8b-100Btok)
• Training method: Supervised fine-tuning (SFT) on L2 learning lessons
• Lesson levels: A1 and A2

Dango is architecturally comparable to the llm-jp-3 family, which adopts a Llama 2-style decoder architecture.

• llm-jp-3-1.8b: https://huggingface.co/llm-jp/llm-jp-3-1.8b

Please visit our GitHub repository for filtering and training code: https://github.com/mattashiho233/dango

License

• Codes and Scripts: CC BY 4.0
• Pre-trained models: CC BY 4.0
• Instruction-tuned models (after L2 acquisition training): Trained with Ichikara instruction data, which prohibits commercial use of derivative models
  • Ichikara data source: https://liat-aip.sakura.ne.jp/wp/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf%e4%bd%9c%e6%88%90/llm%e3%81%ae%e3%81%9f%e3%82%81%e3%81%ae%e6%97%a5%e6%9c%ac%e8%aa%9e%e3%82%a4%e3%83%b3%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%82%b7%e3%83%a7%e3%83%b3%e3%83%87%e3%83%bc%e3%82%bf-%e5%85%ac%e9%96%8b/

Citation

If you use Dango in your research, please cite:

@inproceedings{matta2026anlp,
  author    = {Shiho Matta and Yin Jou Huang and Fei Cheng and Takashi Kodama and Hirokazu Kiyomaru and Yugo Murawaki},
  title     = {Pretraining a Japanese-Only Large Language Model for Studying Second Language Acquisition},
  booktitle = {Proceedings of the Thirty-second Annual Meeting of the Association for Natural Language Processing},
  year      = {2026},
  pages     = {3225--3230},
  publisher = {Association for Natural Language Processing},
  address   = {Utsunomiya, Japan}
}