---
library_name: transformers
license: cc-by-sa-4.0
language:
- en
- ja
base_model:
- EQUES/JPharmatron-7B-base
tags:
- pharmacy
- biology
- chemistry
- medical
---

# JPharmatron-7B

<!-- Provide a quick summary of what the model is/does. -->

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


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

The JPharmatron-7B is continually pre-trained using 8.8B tokens from Japanese and English datasets, based on Qwen2.5-7B. Compared to the JPharmatron-7B-base model, JPharmatron-7B has enhanced chat capabilities, obtained from Qwen2.5-7B-Instruct's chat vector.

- **Developed by:** EQUES Inc.
- **Funded by [optional]:** [GENIAC Project](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html)
- **Model type:** Causal decoder-only
- **Language(s) (NLP):** Japanese, English
- **License:** CC-BY-SA-4.0

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/EQUES-Inc/pharma-LLM-eval
- **Paper [optional]:** [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://arxiv.org/abs/2505.16661)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This model is intended for applications in pharmaceutical paperwork and research. It is not validated for medical use or any other risk-sensitive use.


## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

We evaluated our model, JPharmatron-7B, with other general / domain-specific models of a similar size.

### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[JPharmaBench](https://huggingface.co/collections/EQUES/jpharmabench-680a34acfe96870e41d050d8) and two existing benchmarks (JMMLU (pharma) and IgakuQA) were used.

### Results

Compared to Meditron3-Qwen2.5-7B and Llama3.1-Swallow-8B-Instruct-v0.3, JPharmatron-7B achieved the highest score on all of the five benchmarks.

![](evaluation.png)

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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](https://arxiv.org/abs/2505.16661).

## Model Card Authors [optional]

[@shinnosukeono](https://shinnosukeono.github.io/)