If you’re delving into the world of Vision Language Models, SpaceLLaVA offers a powerful tool to enhance spatial reasoning capabilities in multimodal contexts. In this guide, we’ll walk you through its usage from installation to running inference, ensuring you harness the full potential of this innovative model.

Model Overview

SpaceLLaVA leverages the LLama 3.1 architecture as the backbone, combined with advanced fused features from the DINOv2 and SigLIP modules. Developed by remyx.ai, this model showcases exceptional abilities in inferring spatial relationships between objects, essential for tasks like Visual Question Answering (VQA).

Getting Started with SpaceLLaVA

Model Requirements

• Python
• Docker (for deployment)
• Compatible GPU (for optimal performance)

Installation Steps

Before diving into the usage, you’ll need to set up your environment.

1. Clone the repository:

   git clone https://github.com/remyxai/VQASynth.git

2. Navigate to the project directory:

   cd VQASynth

Running Inference with SpaceLLaVA

Quick Test with Python Script

You can easily test the model using the following script:

python run_inference.py --model_location remyxai/SpaceLlama3.1 --image_source "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg" --user_prompt "What is the distance between the man in the red hat and the pallet of boxes?"

Deployment with Docker

If you’re looking to deploy SpaceLLaVA, the project provides a Dockerized Triton server. Here’s how to get it running:

docker build -f Dockerfile -t spacellava-server:latest
docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 24G spacellama3.1-server:latest
python3 client.py --image_path "https://remyx.ai/assets/spatialvlm/warehouse_rgb.jpg" --prompt "What is the distance between the man in the red hat and the pallet of boxes?"

Understanding the Code: An Analogy

Imagine that you are a librarian who has been given a library filled with books (your model) and a card catalog (your input parameters). The script you execute is like the instruction sheet that tells you how to retrieve a specific book based on user queries. You are given the “book title” (model location), a “section to search” (image source), and a “query” (user prompt). Your job is to follow this instruction to retrieve the requested information accurately.

Troubleshooting

If you encounter issues while running SpaceLLaVA, consider the following:

• Ensure all dependencies are installed properly.
• Confirm that your GPU drivers are up-to-date.
• Check network connections if fetching images from the web.

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Conclusion

SpaceLLaVA serves as a robust framework for understanding spatial relationships in multimodal contexts. By following the steps outlined in this guide, you can effectively utilize this model for various tasks that require enhanced spatial reasoning capabilities.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.