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---
license: apache-2.0
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- openai/clip-vit-large-patch14-336
base_model_relation: merge
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- image-text-to-text
- medical
- Automated Chest X-ray Report Generation
- RRG
- radiology
---
# LLaVA-Med v1.5 (based on [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2))
LLaVA-Med (Large Language and Vision Assistant for bioMedicine) is an open-source large vision-language model adapted for biomedical applications. Built upon LLaVA and enhanced through curriculum learning, LLaVA-Med is fine-tuned specifically for open-ended biomedical question answering tasks.
This release aims to support research reproducibility for the corresponding paper, which demonstrates improved performance on biomedical VQA benchmarks such as **PathVQA** and **VQA-RAD**.
πŸ“Œ Note: For original model weights, refer to [microsoft/llava-med-v1.5-mistral-7b](https://huggingface.co/microsoft/llava-med-v1.5-mistral-7b).
πŸ“ƒ Original paper: [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://arxiv.org/abs/2306.00890).
***
# πŸ”¬ Experimental Usage in Libra's repo
This model checkpoint is intended for **experimental** use and can be tested directly within the [**Libra repository**](https://github.com/X-iZhang/Libra).
## Key Modification
To enable the **re-trained** vision encoder during inference, ensure the following configuration is applied:
```json
"unfreeze_mm_vision_tower": true
```
## πŸ“š Learn More
For a deeper dive into the methodology, theoretical insights, and performance benchmarks of the Libra framework, please see the following resources:
- πŸ”— **Project Website**: [Libra v1.0](https://x-izhang.github.io/Libra_v1.0/)
- πŸ“„ **Paper**: [arXiv:2411.19378](https://arxiv.org/abs/2411.19378)
- πŸ’» **Code Repository**: [X-iZhang/Libra (GitHub)](https://github.com/X-iZhang/Libra)
- πŸ“· **Related Project**: [CCD – Clinical Change Detection](https://x-izhang.github.io/CCD/); see technical details in the paper [here](https://arxiv.org/abs/2509.23379).
---
### License
[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license.
---