JPharmatron-7B-base

JPharmatron-7B-base is a 7B large language model designed for pharmaceutical applications and researches.

Model Details

Model Description

The JPharmatron-7B-base is continually pre-trained using 2B tokens from Japanese datasets, based on Qwen2.5-7B.

• Developed by: EQUES Inc.
• Funded by [optional]: GENIAC Project
• Model type: Causal decoder-only
• Language(s) (NLP): Japanese, English
• License: CC-BY-SA-4.0

Model Sources [optional]

• Repository: https://github.com/EQUES-Inc/pharma-LLM-eval
• Paper [optional]: A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP

Uses

This model has not undergone any post-training including instruction fine-tuning. Therefore, direct use of this model for downstream tasks is not recommended. Also, it is not validated for medical use or any other risk-sensitive use.

Citation [optional]

BibTeX:

@misc{sukeda_japanese_2025,
  title     = {A {Japanese} {Language} {Model} and {Three} {New} {Evaluation} {Benchmarks} for {Pharmaceutical} {NLP}},
  url       = {http://arxiv.org/abs/2505.16661},
  doi       = {10.48550/arXiv.2505.16661},
  abstract  = {We present a Japanese domain-specific language model for the pharmaceutical field, developed through continual pretraining on 2 billion Japanese pharmaceutical tokens and 8 billion English biomedical tokens. To enable rigorous evaluation, we introduce three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task designed to assess consistency reasoning between paired statements. We evaluate our model against both open-source medical LLMs and commercial models, including GPT-4o. Results show that our domain-specific model outperforms existing open models and achieves competitive performance with commercial ones, particularly on terminology-heavy and knowledge-based tasks. Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge. Our benchmark suite offers a broader diagnostic lens for pharmaceutical NLP, covering factual recall, lexical variation, and logical consistency. This work demonstrates the feasibility of building practical, secure, and cost-effective language models for Japanese domain-specific applications, and provides reusable evaluation resources for future research in pharmaceutical and healthcare NLP. Our model, codes, and datasets are released at https://github.com/EQUES-Inc/pharma-LLM-eval.},
  urldate   = {2025-05-30},
  publisher = {arXiv},
  author    = {Sukeda, Issey and Fujii, Takuro and Buma, Kosei and Sasaki, Shunsuke and Ono, Shinnosuke},
  month     = may,
  year      = {2025},
  note      = {arXiv:2505.16661 [cs]},
  annote    = {Comment: 15 pages, 9 tables, 5 figures}
}

More Information [optional]

See our preprint: A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP.

Model Card Authors [optional]

@shinnosukeono