
# 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
@misc{rohanian2023exploring,
      title={Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing}, 
      author={Omid Rohanian and Mohammadmahdi Nouriborji and David A. Clifton},
      year={2023},
      eprint={2401.00579},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
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transformers pytorch llama text-generation biomedical clinical medical arxiv:2401.00579 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us