Welcome to your guide on using the SpaceLLaVA model, an innovative Vision-Language Model designed to enhance spatial reasoning! In this blog post, we’ll break down the setup, usage, and deployment of the model while also providing troubleshooting tips to ensure a smooth experience. Let’s dive in!

Understanding SpaceLLaVA

SpaceLLaVA is crafted using the llama3.1-8B as its backbone and fused with the DINOv2+SigLIP features from prismatic-vlms. The model incorporates data synthesis techniques and publically available models to empower spatial reasoning capabilities. Think of it as a highly skilled architect who can instantly assess and understand the layout of objects within a construction site.

Model Details

• Developed by: remyx.ai
• Model type: MultiModal Model, Vision Language Model, Prismatic-vlms, Llama 3.1
• Fine-tuned from model: Llama 3.1

Getting Started with SpaceLLaVA

To utilize SpaceLLaVA, you can run inference using the provided script. Below is a simple walkthrough on how to execute it:

Step 1: Run the Inference Script

To perform a quick test, use the following command:

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?"

Deploying SpaceLLaVA

If you’re looking to deploy the model, you can do so easily using Docker. Follow these steps:

Step 1: Build the Dockerized Server

docker build -f Dockerfile -t spacellava-server:latest

Step 2: Run the Docker Container

Once the server is built, execute it with the following command:

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?"

Troubleshooting Common Issues

If you encounter any issues while using SpaceLLaVA, here are some troubleshooting steps you can try:

• Problem: Docker containers are not running correctly.
• Solution: Ensure that Docker is installed and you’re using the correct version that supports GPU acceleration.
• Problem: Inference results are unexpected.
• Solution: Double-check the input image and user prompt for accuracy and relevance to the scene depicted.
• Problem: Script execution fails.
• Solution: Verify that all dependencies are installed and that the file paths in your command line are correct.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

SpaceLLaVA is a powerful tool for enhancing spatial reasoning in multimodal assessments. From running inference scripts to deploying on a Docker server, there’s a multitude of ways to interact with this model. With proper setup and careful input, you can explore the fascinating world of spatial reasoning!

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.