Instructions to use EQUES/JPharmatron-7B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EQUES/JPharmatron-7B-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EQUES/JPharmatron-7B-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EQUES/JPharmatron-7B-base")
model = AutoModelForCausalLM.from_pretrained("EQUES/JPharmatron-7B-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EQUES/JPharmatron-7B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EQUES/JPharmatron-7B-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EQUES/JPharmatron-7B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EQUES/JPharmatron-7B-base

SGLang

How to use EQUES/JPharmatron-7B-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EQUES/JPharmatron-7B-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EQUES/JPharmatron-7B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EQUES/JPharmatron-7B-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EQUES/JPharmatron-7B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EQUES/JPharmatron-7B-base with Docker Model Runner:
```
docker model run hf.co/EQUES/JPharmatron-7B-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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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 (IJCNLP-AACL 2025)

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]

This paper has been accepted to IJCNLP-AACL 2025.

BibTeX:

@inproceedings{ono-etal-2025-japanese,
    title = "A {J}apanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical {NLP}",
    author = "Ono, Shinnosuke  and
      Sukeda, Issey  and
      Fujii, Takuro  and
      Buma, Kosei  and
      Sasaki, Shunsuke",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.ijcnlp-long.72/",
    pages = "1316--1332",
    ISBN = "979-8-89176-298-5",
    abstract = "We present **JPharmatron**, a Japanese domain-specific large language model (LLM) for the pharmaceutical field, developed through continual pre-training on two billion Japanese pharmaceutical tokens and eight billion English biomedical tokens. For rigorous evaluation, we introduce **JPharmaBench**, a benchmark suite consisting of three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task involving cross-document consistency checking.We evaluate our model against open-source medical LLMs and commercial models, including GPT-4o. Experimental results show that **JPharmatron** outperforms existing open models and achieves competitive performance with commercial ones.Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge.**JPharmatron** enables secure and local model deployment for pharmaceutical tasks, where privacy and legal constraints limit the use of closed models. Besides, **JPharmaBench** offers a reproducible framework for evaluating Japanese pharmaceutical natural language processing. Together, they demonstrate the feasibility of practical and cost-efficient language models for Japanese healthcare and pharmaceutical sectors.Our model, codes, and datasets are available on HuggingFace: https://huggingface.co/collections/EQUES/jpharmatron and https://huggingface.co/collections/EQUES/jpharmabench."
}