Biomistral 7B SLERP

🚀 BioMistral-7B-slerp

This project is a merged pre-trained language model created using mergekit, aiming to provide more powerful language processing capabilities in the medical and biological fields.

🚀 Quick Start

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

Basic Usage

from transformers import AutoModel, AutoTokenizer

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

✨ Features

• Multi-model Merge: This model is a merge of BioMistral/BioMistral-7B and mistralai/Mistral-7B-Instruct-v0.1, integrating the advantages of multiple models.
• Advanced Merge Method: Using the SLERP merge method to ensure the smoothness and stability of the merged model.
• Multilingual Support: Supports multiple languages such as French, English, Spanish, Italian, Polish, Dutch, and German, facilitating cross - language applications.
• Medical Domain Adaptation: Pre - trained on PubMed data, it is well - adapted to the medical and biological fields.

📦 Installation

The installation mainly involves using the transformers library. You can install it via the following command:

pip install transformers

💻 Usage Examples

The basic usage example has been provided in the "Quick Start" section. You can use the loaded model and tokenizer for text generation, question - answering, and other tasks.

📚 Documentation

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• BioMistral/BioMistral-7B
• mistralai/Mistral-7B-Instruct-v0.1

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.1
        layer_range: [0, 32]
      - model: BioMistral/BioMistral-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

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

Abstract: 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.

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](http://www.idris.fr/jean - zay/) French HPC.

Quantized Models

Supervised Fine - tuning Benchmark

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.

🔧 Technical Details

The model uses the SLERP merge method, which is a spherical linear interpolation method. It can better preserve the characteristics of the original model during the merging process, making the merged model more stable and effective. The model is further pre - trained on PubMed data, which enables it to better understand and process medical and biological texts.

📄 License

This project is licensed under the Apache - 2.0 license.

📚 Citation BibTeX

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

@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}
}

⚠️ Important Note

Although BioMistral is intended to encapsulate medical knowledge sourced from high - quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real - world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real - world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generation or any professional health and medical purposes.