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README.md

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 ---
-license: apache-2.0
+title: Llama2-MedTuned-7b
+emoji: 🧬
+colorFrom: blue
+colorTo: green
+sdk: static
+pinned: false
+license: mit
+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
+@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}
+}