Update README.md

README.md

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 ---
-license: cc-by-nc-4.0
+license: other
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 - medical
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@@ -31,4 +31,50 @@ Users have to apply it on top of the original LLaMA weights to get actual LLaVA
 
 [![Code License](https://img.shields.io/badge/Code%20License-Microsoft%20Research-red)](Research%20License.docx)
 [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)](https://creativecommons.org/licenses/by-nc/4.0/deed.en)
-**Usage and License Notices**: The data, code, and model checkpoints are intended and licensed for research use only. They are also subject to additional restrictions dictated by the Terms of Use: LLaMA, Vicuna and GPT-4 respectively. The data is made available under CC BY NC 4.0. The data, code, and model checkpoints may be used for non-commercial purposes and any models trained using the dataset should be used only for research purposes. It is expressly prohibited for models trained on this data to be used in clinical care or for any clinical decision making purposes.
+**Usage and License Notices**: The data, code, and model checkpoints are intended and licensed for research use only. They are also subject to additional restrictions dictated by the Terms of Use: LLaMA, Vicuna and GPT-4 respectively. The data is made available under CC BY NC 4.0. The data, code, and model checkpoints may be used for non-commercial purposes and any models trained using the dataset should be used only for research purposes. It is expressly prohibited for models trained on this data to be used in clinical care or for any clinical decision making purposes.
+
+## Model Description 
+
+Large Language and Vision Assistant for bioMedicine (i.e., “LLaVA-Med”) is a large language and vision model trained using a curriculum learning method for adapting LLaVA to the biomedical domain. It is an open-source release intended for research use only to facilitate reproducibility of the corresponding paper  which claims improved performance for open-ended biomedical questions answering tasks, including common visual question answering (VQA) benchmark datasets such as PathVQA and VQA-RAD. 
+
+### Model Uses 
+
+#### Intended Use 
+The data, code, and model checkpoints are intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper. The data, code, and model checkpoints are not intended to be used in clinical care or for any clinical decision making purposes. 
+
+#### Primary Intended Use 
+The primary intended use is to support AI researchers reproducing and building on top of this work. LLaVA-Med and its associated models should be helpful for exploring various biomedical vision-language processing (VLP ) and vision question answering (VQA) research questions. 
+
+#### Out-of-Scope Use 
+**Any** deployed use case of the model --- commercial or otherwise --- is out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are intended *for research use only* and not intended for deployed use cases. Please refer to [the associated paper](https://aka.ms/llava-med) for more details. 
+
+### Data 
+This model builds upon [PMC-15M dataset](https://aka.ms/biomedclip-paper), which is a large-scale parallel image-text dataset for biomedical vision-language processing. It contains 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. It covers a diverse range of biomedical image types, such as microscopy, radiography, histology, and more. 
+
+### Limitations 
+This model was developed using English corpora, and thus may be considered English-only. This model is evaluated on a narrow set of biomedical benchmark tasks, described in [LLaVA-Med paper](https://aka.ms/llava-med). As such, it is not suitable for use in any clinical setting. Under some conditions, the model may make inaccurate predictions and display limitations, which may require additional mitigation strategies. In particular, this model is likely to carry many of the limitations of the model from which it is derived, [LLaVA](https://llava-vl.github.io/).
+
+Further, this model was developed in part using the [PMC-15M](https://aka.ms/biomedclip-paper) dataset. The figure-caption pairs that make up this dataset may contain biases reflecting the current practice of academic publication. For example, the corresponding papers may be enriched for positive findings, contain examples of extreme cases, and otherwise reflect distributions that are not representative of other sources of biomedical data. 
+
+
+## Acknowledgement
+
+- Our project is built upon [LLaVA](https://github.com/lm-sys/FastChat) and [Vicuna](https://github.com/lm-sys/FastChat): They provide our base models with the amazing multimodal and langauge capabilities, respectively!
+
+If you find LLaVA-Med useful for your your research and applications, please cite using this BibTeX:
+```bibtex
+@article{li2023llavamed,
+  title={Llava-med: Training a large language-and-vision assistant for biomedicine in one day},
+  author={Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng},
+  journal={arXiv preprint arXiv:2306.00890},
+  year={2023}
+}
+```
+
+
+## Related Projects
+
+- [LLaVA](https://llava-vl.github.io/)
+- [BioMed CLIP](https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224)
+- [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+