---
title: Llama2-MedTuned-7b
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: static
pinned: false
license: apache-2.0
tags:
- biomedical 
- clinical
- medical
---

# Model Description
Llama2-MedTuned-7b is an instruction-tuned version of the Llama2 7B model, specifically adapted for biomedical language processing tasks. It has been fine-tuned on a dataset consisting of approximately 200,000 instruction-focused samples, covering a range of biomedical and clinical NLP tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI).

# Instruction Tuning Procedure
This model underwent instruction tuning, a process where the model is fine-tuned with detailed instructions to enhance its ability to interpret and execute specific tasks in the biomedical domain. The tuning involved the use of a comprehensive instruction-based dataset, tailor-made to align with the requirements of biomedical NLP tasks.

# Model Capabilities
Llama2-MedTuned-7b demonstrates an enhanced understanding of biomedical contexts, effectively handling NER, RE, and NLI tasks. It showcases improved accuracy in generating structured outputs suitable for evaluation using conventional metrics.

# Architecture
The architecture of Llama2-MedTuned-7b is based on the autoregressive transformer model Llama2 7B. This model maintains the original transformer layers and attention mechanisms, specifically adjusted to cater to the linguistic intricacies of the biomedical field.

# Citation
If you utilise Llama2-MedTuned-7b in your research or application, please consider citing our paper:

```bibtex
@article{rohanian2024exploring,
  title = {Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing},
  author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Nooralahzadeh, Farhad and Clifton, Lei and Clifton, David A},
  journal = {Artificial Intelligence in Medicine},
  volume = {158},
  pages = {103007},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.artmed.2024.103007},
  url = {https://www.sciencedirect.com/science/article/pii/S0933365724002495},
  issn = {0933-3657}
}
```