You can deploy this model and start earning money today!

yuhos16/
SkinGPT-R1

dermatology

medical

multimodal

vision-language-model

Views

No views yet

Trustworthy and Fair SkinGPT-R1 for Democratizing Dermatological Reasoning across Diverse Ethnicities

CUHKSZ Logo

SkinGPT-R1 is a dermatological reasoning vision language model. 🩺✨

The Chinese University of Hong Kong, Shenzhen

Updates

We will soon release the SkinGPT-R1-7B weights.

Overview

text

SkinGPT-R1/
├── checkpoints/
├── environment.yml
├── inference/
│   ├── full_precision/
│   └── int4_quantized/
├── requirements.txt
└── README.md

This repo provides full-precision inference, INT4 quantized inference, multi-turn chat, and FastAPI serving.

Install

environment.yml is a Conda environment definition file for reproducing the recommended runtime environment.

From scratch:

bash

git clone https://huggingface.co/yuhos16/SkinGPT-R1
cd SkinGPT-R1
conda env create -f environment.yml
conda activate skingpt-r1

Manual setup:

bash

git clone https://huggingface.co/yuhos16/SkinGPT-R1
cd SkinGPT-R1
conda create -n skingpt-r1 python=3.10.20 -y
conda activate skingpt-r1
pip install -r requirements.txt

Quick Start

Use the repository ./checkpoints directory as the model weights directory.
Prepare a test image, for example ./test_images/lesion.jpg.
Run a first test.

Full precision:

bash inference/full_precision/run_infer.sh --image ./test_images/lesion.jpg

INT4:

bash inference/int4_quantized/run_infer.sh --image_path ./test_images/lesion.jpg

Usage

Mode	Full Precision	INT4 Quantized
Single image	`bash inference/full_precision/run_infer.sh --image ./test_images/lesion.jpg`	`bash inference/int4_quantized/run_infer.sh --image_path ./test_images/lesion.jpg`
Multi-turn chat	`bash inference/full_precision/run_chat.sh --image ./test_images/lesion.jpg`	`bash inference/int4_quantized/run_chat.sh --image ./test_images/lesion.jpg`
API service	`bash inference/full_precision/run_api.sh`	`bash inference/int4_quantized/run_api.sh`

Default API ports:

Full precision: 5900
INT4 quantized: 5901

Notes

On multi-GPU servers, prepend commands with CUDA_VISIBLE_DEVICES=0 if you want to pin one GPU.
RTX 50 series should use the default sdpa path.
A100 / RTX 3090 / RTX 4090 / H100 can also try flash_attention_2 if their CUDA stack supports it.

API Endpoints

Both API services expose the same endpoints:

POST /v1/upload/{state_id}
POST /v1/predict/{state_id}
POST /v1/reset/{state_id}
POST /diagnose/stream
GET /health

Disclaimer

This project is for research and educational use only. It is not a substitute for professional medical advice, diagnosis, or treatment. ⚠️

License

This repository is released under the MIT License. See LICENSE for details.