# Asset from the SCALEMED Framework

This model/dataset is an asset released as part of the **SCALEMED** framework, a project focused on developing scalable and resource-efficient medical AI assistants.

## Project Overview

The models, known as **DermatoLlama**, were trained on versions of the **DermaSynth** dataset, which was also generated using the SCALEMED pipeline.

For a complete overview of the project, including all related models, datasets, and the source code, please visit our main Hugging Face organization page:
**[https://huggingface.co/DermaVLM](https://huggingface.co/DermaVLM)**

## Usage

```python
from transformers import MllamaForConditionalGeneration, AutoProcessor
from peft import PeftModel

# Load base model
base_model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct" 
model = MllamaForConditionalGeneration.from_pretrained(base_model_name)
processor = AutoProcessor.from_pretrained(base_model_name)

# Load LoRA adapter
adapter_path = "DermaVLM/DermatoLLama-50k"
model = PeftModel.from_pretrained(model, adapter_path)
```

## Citation

If you use this model, dataset, or any other asset from our work in your research, we kindly ask that you please cite our preprint:

```bibtex
@article{Yilmaz_2024,
	author = {Yilmaz, Abdurrahim and Yuceyalcin, Furkan and Varol, Rahmetullah and Gokyayla, Ece and Erdem, Ozan and Choi, Donghee and Demircali, Ali Anil and Gencoglan, Gulsum and Posma, Joram M. and Temelkuran, Burak},
	doi = {10.1101/2025.05.17.25327785},
	journal = {medRxiv},
	title = {A synthetic data generation framework for scalable and resource-efficient medical AI assistants},
	url = {https://www.medrxiv.org/content/10.1101/2025.05.17.25327785v1},
	year = {2025}
}
```