How to Use SpaceMinitron-4B for Enhanced Spatial Reasoning

August 21, 2024

Have you ever wanted a powerful tool that combines spatial reasoning and multimodal analysis? Look no further! Introducing SpaceMinitron-4B, a state-of-the-art model built on the Minitron-4B-Base backbone and enhanced with advanced features. This guide will walk you through the details of this amazing model, its usage, and troubleshooting tips.

Model Overview

SpaceMinitron-4B integrates innovative data synthesis techniques to boost spatial reasoning capabilities, making it a must-have for anyone working with multimodal models. This is particularly useful for solving visual question answering tasks (VQA) related to spatial relationships.

Key Features

Developed By: remyx.ai
Model Type: MultiModal Model, Vision Language Model
Fine-tuned From: Minitron-4B-Base
Primary Dataset: SpaceLLaVA
Research Paper: SpatialVLM

How to Use SpaceMinitron-4B

To run this model, you will be using the run_inference.py script. Here’s how you can get started:

Running the Inference Script

Execute the following command in your terminal:

bash
python run_inference.py --model_location remyxai/SpaceMinitron-4B \
--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 with Docker

For a smooth deployment, navigate to the docker directory and run the commands below:

bash
docker build -f Dockerfile -t spacellava-server:latest
docker run -it --rm --gpus all -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 24G spaceminitron-4B-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

Think of SpaceMinitron-4B like a highly skilled detective at a crime scene. Just as a detective examines the evidence and asks precise questions about relationships between the suspects and objects, SpaceMinitron-4B analyzes images and queries to determine spatial relations between objects. The model uses hints (prompts) to investigate and retrieve its conclusions about the scene, providing insights that help users understand complex spatial dynamics.

Troubleshooting Tips

If you encounter issues while using SpaceMinitron-4B, consider the following troubleshooting tips:

Ensure that the Docker installation is correctly configured and that your GPU drivers are up-to-date.
Check the paths in your command to make sure they lead to the correct image sources and models.
Make sure your questions are clear and contextually relevant to the images provided.

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

Conclusion

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.