Instructions to use GAD-Research-Lab/MedicalAI-Light-Weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

MedicalAI-Light-Weight / config.json

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	{
	"_comment": "MedicalAI - Light Weight configuration. Edit this file instead of Python code.",

	"device": "auto",
	"cpu_threads": "auto",
	"use_fp16": "auto",
	"use_onnx": true,

	"data_dir": "./data",
	"csv_path": "./data/dataset.csv",
	"images_dir": "./data/images",

	"checkpoint_dir": "./checkpoints",
	"onnx_dir": "./checkpoints/onnx",
	"onnx_full_dir": "./checkpoints/onnx_full",

	"blip_model_dir": "./blip-xray-finetuned",
	"blip_model_name": "Salesforce/blip-image-captioning-base",

	"fusion_checkpoint": "./checkpoints/fusion_model.pth",

	"confidence_threshold": 0.75,
	"max_symptom_length": 64,
	"web_port": 7860,
	"web_host": "127.0.0.1",
	"api_port": 8000,
	"api_host": "127.0.0.1",
	"api_workers": 2
	}