Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.

1. BioMistral models

BioMistral is a suite of Mistral-based further pre-trained open source models suited for the medical domains and pre-trained using textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA, and CC BY-ND). All the models are trained using the CNRS (French National Centre for Scientific Research) Jean Zay French HPC.

Model Name	Base Model	Model Type	Sequence Length	Download
BioMistral-7B	Mistral-7B-Instruct-v0.1	Further Pre-trained	2048	HuggingFace
BioMistral-7B-DARE	Mistral-7B-Instruct-v0.1	Merge DARE	2048	HuggingFace
BioMistral-7B-TIES	Mistral-7B-Instruct-v0.1	Merge TIES	2048	HuggingFace
BioMistral-7B-SLERP	Mistral-7B-Instruct-v0.1	Merge SLERP	2048	HuggingFace

2. Quantized Models

Base Model	Method	q_group_size	w_bit	version	VRAM GB	Time	Download
BioMistral-7B	FP16/BF16				15.02	x1.00	HuggingFace
BioMistral-7B	AWQ	128	4	GEMM	4.68	x1.41	HuggingFace
BioMistral-7B	AWQ	128	4	GEMV	4.68	x10.30	HuggingFace
BioMistral-7B	BnB.4		4		5.03	x3.25	HuggingFace
BioMistral-7B	BnB.8		8		8.04	x4.34	HuggingFace
BioMistral-7B-DARE	AWQ	128	4	GEMM	4.68	x1.41	HuggingFace
BioMistral-7B-TIES	AWQ	128	4	GEMM	4.68	x1.41	HuggingFace
BioMistral-7B-SLERP	AWQ	128	4	GEMM	4.68	x1.41	HuggingFace

2. Using BioMistral

You can use BioMistral with Hugging Face's Transformers library as follow.

Loading the model and tokenizer :

python

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
model = AutoModel.from_pretrained("BioMistral/BioMistral-7B")

3. Supervised Fine-tuning Benchmark

	Clinical KG	Medical Genetics	Anatomy	Pro Medicine	College Biology	College Medicine	MedQA	MedQA 5 opts	PubMedQA	MedMCQA	Avg.
BioMistral 7B	59.9	64.0	56.5	60.4	59.0	54.7	50.6	42.8	77.5	48.1	57.3
Mistral 7B Instruct	62.9	57.0	55.6	59.4	62.5	57.2	42.0	40.9	75.7	46.1	55.9

BioMistral 7B Ensemble	62.8	62.7	57.5	63.5	64.3	55.7	50.6	43.6	77.5	48.8	58.7
BioMistral 7B DARE	62.3	67.0	55.8	61.4	66.9	58.0	51.1	45.2	77.7	48.7	59.4
BioMistral 7B TIES	60.1	65.0	58.5	60.5	60.4	56.5	49.5	43.2	77.5	48.1	57.9
BioMistral 7B SLERP	62.5	64.7	55.8	62.7	64.8	56.3	50.8	44.3	77.8	48.6	58.8

MedAlpaca 7B	53.1	58.0	54.1	58.8	58.1	48.6	40.1	33.7	73.6	37.0	51.5
PMC-LLaMA 7B	24.5	27.7	35.3	17.4	30.3	23.3	25.5	20.2	72.9	26.6	30.4
MediTron-7B	41.6	50.3	46.4	27.9	44.4	30.8	41.6	28.1	74.9	41.3	42.7
BioMedGPT-LM-7B	51.4	52.0	49.4	53.3	50.7	49.1	42.5	33.9	76.8	37.6	49.7

GPT-3.5 Turbo 1106*	74.71	74.00	65.92	72.79	72.91	64.73	57.71	50.82	72.66	53.79	66.0

Supervised Fine-Tuning (SFT) performance of BioMistral 7B models compared to baselines, measured by accuracy (↑) and averaged across 3 random seeds of 3-shot. DARE, TIES, and SLERP are model merging strategies that combine BioMistral 7B and Mistral 7B Instruct. Best model in bold, and second-best underlined. *GPT-3.5 Turbo performances are reported from the 3-shot results without SFT.

Citation BibTeX

Arxiv : https://arxiv.org/abs/2402.10373

bibtex

@misc{labrak2024biomistral,
      title={BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains}, 
      author={Yanis Labrak and Adrien Bazoge and Emmanuel Morin and Pierre-Antoine Gourraud and Mickael Rouvier and Richard Dufour},
      year={2024},
      eprint={2402.10373},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

LoneStriker/BioMistral-7B-GPTQ

BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

1. BioMistral models

2. Quantized Models

2. Using BioMistral

3. Supervised Fine-tuning Benchmark

Citation BibTeX

LoneStriker/
BioMistral-7B-GPTQ