Hands-On Adventure: Setting Up and Running Your LLaVA Large Multimodal Model (LMM)

Photo by Harley-Davidson on Unsplash

Welcome back, fellow AI enthusiasts! In our last blog, we took a high-flying overview of the components that make Large Multimodal Models (LMMs) so powerful. Now, it’s time to roll up our sleeves and get into the nitty-gritty of setting one up. Whether you’re just starting out or already knee-deep in AI, this guide will walk you through everything you need to know to get your LMM up and running. And don’t worry — there’s still more to come in our third blog, so stay tuned!

🔧 Step 1: Setting Up — Let’s Get This Party Started!

First things first — before we dive into code, we need to get our environment ready. Here’s what you’ll need:

1. Python 🐍: Make sure you’ve got Python 3.7 or above installed. You can grab it here.

2. Clone the Repo 🛠️: Jump into your terminal (or command prompt for Windows users) and clone the LLaVA-OneVision repository:

git clone https://github.com/yourusername/LLaVA-OneVision.git
cd LLaVA-OneVision

3. Install Dependencies 📦: Next, install all the required packages with pip:

pip install -r requirements.txt

🏃‍♂️ Step 2: Running the Demo — Seeing is Believing!

Now that everything is set up, let’s run a quick demo to see our LMM in action. This is where the magic happens — watch as the model processes images and text together!

1. Launch the Demo 🚀:

python demo.py --config configs/demo.yaml

This command will kickstart the demo, loading up the pre-trained model and running through a few example scenarios.

2. Interactive Mode 💬: Want to ask the model something specific? You can switch to an interactive mode where you input text prompts and see how the model responds. Just use:

python demo.py --interactive

🛠️ Step 3: Installation — Getting Everything In Place

If you want to customize or train the model further, a more in-depth installation is required.

1. Environment Setup 🌐:

• Virtual Environment: It’s always a good idea to set up a virtual environment to keep things tidy:

python -m venv llava-env
source llava-env/bin/activate  # On Windows use `llava-env\Scripts\activate`

• Install CUDA: If you’ve got a GPU, make sure CUDA is installed for faster processing.

2. Additional Dependencies 📚:

• Depending on your use case (like using different datasets or advanced configurations), you might need some extra packages. These can usually be installed with pip as well:

pip install <additional-package>

🧪 Step 4: Inference — Let’s Make Predictions!

Now that your environment is set up and the demo is running smoothly, it’s time to perform some inference with the model. This is where we put the LMM to work on new data.

1. Prepare Your Data 📄: Make sure your input images and text are ready. The model needs well-formatted data to do its job.
2. Run Inference 🔍:

from llava_onevision import LLaVAOneVision

# Load the pre-trained model
model = LLaVAOneVision.load_pretrained('path/to/checkpoint')

# Prepare your input (replace with actual data paths)
image_path = 'path/to/image.jpg'
text_input = 'Describe the image content.'

# Run the model
output = model.infer(image_path, text_input)
print(output)

This code snippet shows how to load a pre-trained model and run inference on an image with some text input. The output should be a natural language description or response based on the input.

📚 What’s Next?

Now that you’ve got your LMM set up and running, you might be itching to dive deeper into customization and fine-tuning. But hold on — there’s so much more we can do! In our third and final blog, we’ll explore how to fine-tune the model on your own data, tweak the settings, and push the limits of what these models can do. So, keep that excitement up — we’re just getting started!

Thanks for sticking around! If you have any questions or run into any issues, drop a comment below.

That wraps up the second blog! What do you think? Ready for more hands-on fun in the next one? Let me know in the comments, and stay tuned for our deep dive into fine-tuning! 😊