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---
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}
}
``` |