ColNomic Embed Multimodal 3B
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Model Type | Multimodal Embedding Model |
| Architecture | Vision-Language Model with unified text and image processing |
| Model URL | https://huggingface.co/nomic-ai/colnomic-embed-multimodal-3b |
| Performance | 61.2 NDCG@5 on Vidore-v2 |
What is colnomic-embed-multimodal-3b?
ColNomic Embed Multimodal 3B is a cutting-edge multimodal embedding model designed specifically for visual document retrieval tasks. Fine-tuned from Qwen2.5-VL 3B Instruct, this model represents a significant advancement in unified text and image processing, capable of handling complex document structures without requiring extensive preprocessing.
Implementation Details
The model utilizes an advanced architecture that processes both text and images in a unified manner, implementing same-source sampling for creating challenging in-batch negatives and offering multi-vector output options for enhanced performance. Built on a 3B parameter foundation, it seamlessly integrates with RAG workflows and provides direct document embedding capabilities.
- Unified text-image encoding without complex preprocessing
- Flash Attention 2 support for optimal performance
- Multi-vector output configuration for improved accuracy
- Direct integration with RAG pipelines
Core Capabilities
- Advanced visual document retrieval with 61.2 NDCG@5 performance
- Processing of research papers, technical documentation, and financial reports
- Handling of equations, diagrams, tables, and multilingual content
- Direct embedding of document page images without OCR
- Support for various document types including product catalogs and visual-rich content
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to directly encode interleaved text and images without complex preprocessing, combined with its state-of-the-art performance in visual document retrieval, sets it apart from traditional approaches. Its multi-vector configuration and same-source sampling strategy create more challenging training scenarios, resulting in more robust embeddings.
Q: What are the recommended use cases?
The model excels in scenarios involving research papers with equations and diagrams, technical documentation with code blocks and flowcharts, financial reports with charts and graphs, and any content where layout and visual information play crucial roles. It's particularly effective for multilingual documents where visual context provides important cues.
