Granite Vision 3.1
Property | Value |
---|---|
Parameter Count | 3 billion |
License | Apache 2.0 |
Release Date | Jan 31st, 2025 |
Languages | English |
What is granite-vision-3.1-2b-preview?
Granite Vision 3.1 is a sophisticated vision-language model specifically engineered for enterprise document understanding and analysis. Built on IBM's Granite LLM architecture, this model excels at processing visual documents, including tables, charts, infographics, and diagrams. With 3 billion parameters, it achieves state-of-the-art performance across various document understanding benchmarks while maintaining computational efficiency.
Implementation Details
The model architecture combines three key components: a SigLIP vision encoder, a two-layer MLP vision-language connector with GELU activation, and the granite-3.1-2b-instruct language model featuring 128k context length. The implementation builds upon LLaVA architecture with enhanced multi-layer encoder features and denser grid resolution for improved document interpretation.
- Trained on IBM's Blue Vela supercomputing cluster with H100 GPUs
- Leverages both public datasets and synthetic data for document understanding
- Implements advanced safety features through Granite Guardian integration
Core Capabilities
- Superior performance on ChartQA (0.86) and DocVQA (0.88) benchmarks
- Enhanced OCR capabilities with 0.75 score on OCRBench
- Strong general VQA performance (0.81 on VQAv2)
- Specialized in table and chart analysis
- Document content extraction and comprehension
Frequently Asked Questions
Q: What makes this model unique?
The model's specialized focus on document understanding, combined with its compact size and superior benchmark performance, makes it particularly valuable for enterprise applications. Its integration with Granite Guardian for safety monitoring sets it apart from similar models.
Q: What are the recommended use cases?
The model is ideal for enterprise applications involving document analysis, including table extraction, chart interpretation, and OCR tasks. It's particularly well-suited for automated content extraction from business documents, technical diagrams, and financial reports.