ColNomic Embed Multimodal 7B
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Multimodal Embedding Model |
| Architecture | Vision-Language Model with unified text-image processing |
| Source Model | Fine-tuned from Qwen2.5-VL 7B Instruct |
| Model URL | huggingface.co/nomic-ai/colnomic-embed-multimodal-7b |
What is colnomic-embed-multimodal-7b?
ColNomic Embed Multimodal 7B represents a breakthrough in visual document retrieval, offering state-of-the-art performance with a unified approach to processing both text and images. The model achieves an impressive 62.7 NDCG@5 on Vidore-v2, surpassing all competing models in the field. Its architecture enables direct encoding of interleaved text and images without requiring complex preprocessing steps.
Implementation Details
Built on a 7B parameter foundation and fine-tuned from Qwen2.5-VL 7B Instruct, the model implements innovative features like same-source sampling for creating challenging in-batch negatives and multi-vector output options for enhanced performance. The architecture unifies text and image processing, making it particularly effective for complex document retrieval tasks.
- Multi-vector state-of-the-art performance across various benchmarks
- Direct document embedding without OCR requirements
- Seamless integration with RAG workflows
- Flash Attention 2 support for optimal performance
Core Capabilities
- Superior performance on visual document retrieval tasks
- Efficient processing of research papers, technical documentation, and financial reports
- Handling of complex layouts including equations, diagrams, and tables
- Multi-language support with visual context understanding
- Direct encoding of charts, graphs, and numerical data
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its ability to process interleaved text and images without complex preprocessing, achieving superior performance through its multi-vector output configuration and same-source sampling approach. It outperforms other models across various benchmarks, particularly in visual document retrieval tasks.
Q: What are the recommended use cases?
The model excels in handling research papers with equations and diagrams, technical documentation with code blocks and flowcharts, product catalogs, financial reports with charts, and multilingual documents where visual context provides important cues. It's particularly effective for content where layout and visual information play crucial roles.
