transformers safetensors phi text-generation nlp code biology medical custom_code en arxiv:2406.06331 license:mit autotrain_compatible text-generation-inference region:us
---
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
- biology
- medical
inference: false
---

## Model Summary

MedPhi-2 is a Phi-2, **2.7 billion** parameters, further trained for the biomedical domain. 
It was proposed in MedExQA paper.

<h1>  🧑‍⚕️ MedExQA  </h1>
<h3> Medical Question Answering Benchmark with Multiple Explanations </h3>

<p>
 📄 <a href="https://arxiv.org/abs/2406.06331" target="_blank">Paper</a> • ⏬ <a href="https://huggingface.co/datasets/bluesky333/MedExQA" target="_blank">Dataset</a>  • ⚕️ <a href="https://huggingface.co/bluesky333/medphi2" target="_blank">MedPhi2</a><br>
</p>


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Model type:** Clinical LLM (Large Language Model)
- **Language(s) (NLP):** English
- **License:**  [MIT license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE)
- **Finetuned from model [optional]:** Phi-2



## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@article{kim2024medexqa,
  title={MedExQA: Medical Question Answering Benchmark with Multiple Explanations},
  author={Kim, Yunsoo and Wu, Jinge and Abdulle, Yusuf and Wu, Honghan},
  journal={arXiv e-prints},
  pages={arXiv--2406},
  year={2024}
}
```
