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MONAI Vision Language Models

The repository provides a collection of vision language models, benchmarks, and related applications, released as part of Project MONAI (Medical Open Network for Artificial Intelligence).

💡 News

[2024/10/31] We released the VILA-M3-3B, VILA-M3-8B, and VILA-M3-13B checkpoints on HuggingFace.
[2024/10/24] We presented VILA-M3 and the VLM module in MONAI at MONAI Day (slides, recording - coming soon!)
[2024/10/24] Interactive VILA-M3 Demo is available online!

VILA-M3

VILA-M3 is a vision language model designed specifically for medical applications. It focuses on addressing the unique challenges faced by general-purpose vision-language models when applied to the medical domain and integrated with existing expert segmentation and classification models.

For details, see here.

Online Demo

Please visit the VILA-M3 Demo to try out a preview version of the model.

Local Demo

Prerequisites

Recommended: Build Docker Container

To run the demo, we recommend building a Docker container with all the requirements. We use a base image with cuda preinstalled.
```
docker build --network=host --progress=plain -t monai-m3:latest -f m3/demo/Dockerfile .
```
Run the container
```
docker run -it --rm --ipc host --gpus all --net host monai-m3:latest bash
```
Note: If you want to load your own VILA checkpoint in the demo, you need to mount a folder using -v <your_ckpts_dir>:/data/checkpoints in your docker run command.
Next, follow the steps to start the Gradio Demo.

Alternative: Manual installation

Linux Operating System
CUDA Toolkit 12.2 (with nvcc) for VILA.

To verify CUDA installation, run:
```
nvcc --version
```
If CUDA is not installed, use one of the following methods:
- Recommended Use the Docker image: nvidia/cuda:12.2.2-devel-ubuntu22.04
```
docker run -it --rm --ipc host --gpus all --net host nvidia/cuda:12.2.2-devel-ubuntu22.04 bash
```
- Manual Installation (not recommended) Download the appropiate package from NVIDIA offical page
Python 3.10 Git Wget and Unzip:

To install these, run
```
sudo apt-get update
sudo apt-get install -y wget python3.10 python3.10-venv python3.10-dev git unzip
```
NOTE: The commands are tailored for the Docker image nvidia/cuda:12.2.2-devel-ubuntu22.04. If using a different setup, adjust the commands accordingly.
GPU Memory: Ensure that the GPU has sufficient memory to run the models:
- VILA-M3: 8B: ~18GB, 13B: ~30GB
- CXR: This expert dynamically loads various TorchXRayVision models and performs ensemble predictions. The memory requirement is roughly 1.5GB in total.
- VISTA3D: This expert model dynamically loads the VISTA3D model to segment a 3D-CT volume. The memory requirement is roughly 12GB, and peak memory usage can be higher, depending on the input size of the 3D volume.
- BRATS: (TBD)

Setup Environment: Clone the repository, set up the environment, and download the experts' checkpoints:

git clone https://github.com/Project-MONAI/VLM --recursive
cd VLM
python3.10 -m venv .venv
source .venv/bin/activate
make demo_m3

Running the Gradio Demo

Navigate to the demo directory:
```
cd m3/demo
```
Start the Gradio demo:

This will automatically download the default VILA-M3 checkpoint from Hugging Face.
```
python gradio_m3.py
```

Alternative: Start the Gradio demo with a local checkpoint, e.g.:

python gradio_m3.py  \
--source local \
--modelpath /data/checkpoints/<8B-checkpoint-name> \
--convmode llama_3

For details, see the available commmandline arguments.

Adding your own expert model

This is still a work in progress. Please refer to the README for more details.

Contributing

To lint the code, please install these packages:

pip install -r requirements-ci.txt

Then run the following command:

isort --check-only --diff .  # using the configuration in pyproject.toml
black . --check  # using the configuration in pyproject.toml
ruff check .  # using the configuration in ruff.toml

To auto-format the code, run the following command:

isort . && black . && ruff format .