Image-Text-to-Text
Transformers
ONNX
Safetensors
English
medical
chest-xray
radiology
clip
blip
multimodal
cpu
Instructions to use GAD-Research-Lab/MedicalAI-Light-Weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GAD-Research-Lab/MedicalAI-Light-Weight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="GAD-Research-Lab/MedicalAI-Light-Weight")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GAD-Research-Lab/MedicalAI-Light-Weight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GAD-Research-Lab/MedicalAI-Light-Weight with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GAD-Research-Lab/MedicalAI-Light-Weight" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GAD-Research-Lab/MedicalAI-Light-Weight", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GAD-Research-Lab/MedicalAI-Light-Weight
- SGLang
How to use GAD-Research-Lab/MedicalAI-Light-Weight with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "GAD-Research-Lab/MedicalAI-Light-Weight" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GAD-Research-Lab/MedicalAI-Light-Weight", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "GAD-Research-Lab/MedicalAI-Light-Weight" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GAD-Research-Lab/MedicalAI-Light-Weight", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GAD-Research-Lab/MedicalAI-Light-Weight with Docker Model Runner:
docker model run hf.co/GAD-Research-Lab/MedicalAI-Light-Weight
File size: 2,525 Bytes
e93bfbd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | #!/usr/bin/env bash
# One-click launcher for MedicalAI on Linux/Mac
set -e
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
cd "$SCRIPT_DIR"
# ββ Check Python ββ
PYTHON=""
for cmd in python3 python; do
if command -v "$cmd" &>/dev/null; then
ver=$("$cmd" --version 2>&1)
if echo "$ver" | grep -qE "Python 3\.(1[0-9]|[0-9]+)"; then
PYTHON="$cmd"
break
fi
fi
done
if [ -z "$PYTHON" ]; then
echo "Error: Python 3.10+ is required but not found."
echo "Install from: https://www.python.org/downloads/"
read -rp "Press Enter to exit"
exit 1
fi
echo "Using: $($PYTHON --version)"
# ββ Virtual Environment ββ
if [ ! -d ".venv" ]; then
echo "Creating virtual environment..."
$PYTHON -m venv .venv
fi
source .venv/bin/activate
# ββ Install Dependencies ββ
if [ -f "requirements.txt" ]; then
echo "Installing dependencies..."
pip install -q -r requirements.txt 2>/dev/null || pip install -r requirements.txt
fi
# ββ Check / Generate Default Models ββ
if [ ! -f "checkpoints/fusion_model.pth" ] && \
[ ! -f "checkpoints/onnx_full/fusion_full.onnx" ] && \
[ ! -f "models/default/fusion_classifier.onnx" ]; then
echo "No models found. Generating default models..."
python setup_default.py
echo "Default models generated. The app will work immediately."
fi
if [ -f "checkpoints/fusion_model.pth" ]; then
echo "Trained model found."
elif [ -f "checkpoints/onnx_full/fusion_full.onnx" ]; then
echo "ONNX pipeline found."
elif [ -f "models/default/fusion_classifier.onnx" ]; then
echo "Default model found. Train for accurate results."
fi
# ββ Launch ββ
echo ""
echo "MedicalAI - Light Weight"
echo "========================"
echo "1) Web UI (recommended - opens in browser)"
echo "2) Command-line interface (CLI)"
echo "3) API Server (for website/app integration)"
echo ""
read -rp "Select (1, 2, or 3): " choice
if [ "$choice" = "2" ]; then
echo "Launching CLI..."
python run.py
elif [ "$choice" = "3" ]; then
echo "Launching API server on http://127.0.0.1:8000 ..."
echo "Your website can connect to: http://127.0.0.1:8000/api"
pip install -q "fastapi[standard]" uvicorn 2>/dev/null || pip install "fastapi[standard]" uvicorn
python quantization.py --mode serve-api
else
if python -c "import gradio" 2>/dev/null; then
python web_ui.py
else
echo "Installing gradio..."
pip install gradio
python web_ui.py
fi
fi
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