rsoohyun/SpatialBlock-3B-reason
SpatialBlock-3B-reason is a 3 billion parameter vision-language model developed by rsoohyun, fine-tuned from Qwen2.5-VL-3B-Instruct. It specializes in spatial reasoning tasks, including 3D-to-2D projection, viewpoint transformation, and structural combination, by directly predicting answers. The model was trained on the synthetic SpatialBlock-15k dataset and supports a context length of 32768 tokens, making it suitable for complex visual-spatial problem-solving.
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SpatialBlock-3B-reason Overview
SpatialBlock-3B-reason is a 3 billion parameter vision-language model (VL-LLM) developed by rsoohyun, specifically designed to enhance spatial intelligence. It is a fine-tuned version of the Qwen2.5-VL-3B-Instruct model, leveraging the synthetic SpatialBlock-15k dataset for specialized training. This model excels at directly predicting answers for various spatial reasoning challenges, differentiating it from general-purpose VL-LLMs.
Key Capabilities
- Advanced Spatial Reasoning: Directly addresses complex spatial problems.
- 3D-to-2D Projection: Capable of understanding and transforming 3D structures into 2D representations.
- Viewpoint Transformation: Can interpret and predict how objects appear from different perspectives.
- Structural Combination: Handles tasks involving the assembly or analysis of combined structures.
- High Context Length: Supports a 32768-token context, allowing for detailed visual and textual inputs.
Good For
- Research in Spatial AI: Ideal for studies focusing on visual-spatial intelligence in AI.
- Robotics and Navigation: Potential applications in understanding and manipulating objects in 3D space.
- Computer Vision Tasks: Enhancing systems that require deep spatial comprehension beyond simple object recognition.
- Synthetic Data Training: Demonstrates effective fine-tuning on specialized synthetic datasets for targeted capabilities.
For more detailed information on training and evaluation, refer to the GitHub repository.