Nomic Embed Multimodal 7B
| Property | Value |
|---|---|
| Parameter Count | 7 Billion |
| Model Type | Multimodal Embedding Model |
| Architecture | Vision-Language Model with unified text-image processing |
| Model URL | https://huggingface.co/nomic-ai/nomic-embed-multimodal-7b |
What is nomic-embed-multimodal-7b?
Nomic Embed Multimodal 7B is a cutting-edge dense multimodal embedding model specifically designed for visual document retrieval tasks. Fine-tuned from Qwen2.5-VL 7B Instruct, this model represents a significant advancement in unified text and image processing, achieving state-of-the-art performance with 58.8 NDCG@5 on Vidore-v2.
Implementation Details
The model employs an advanced architecture that enables direct encoding of interleaved text and images without complex preprocessing steps. It utilizes innovative training techniques including same-source sampling for creating harder in-batch negatives and sophisticated hard negative mining with positive-aware techniques.
- Unified text-image encoding capability
- Flash Attention 2 support for optimal performance
- Direct document embedding without OCR requirements
- Seamless integration with RAG workflows
Core Capabilities
- Superior performance across multiple document types including research papers, technical documentation, and financial reports
- Efficient processing of complex visual layouts including equations, diagrams, and tables
- Multi-language support with strong emphasis on English content
- Direct handling of charts, graphs, and numerical data in financial documents
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to process both text and images in a unified manner, combined with its state-of-the-art performance and sophisticated training approach using hard negative mining and same-source sampling, sets it apart from traditional document retrieval systems.
Q: What are the recommended use cases?
The model excels in scenarios involving research papers, technical documentation, product catalogs, financial reports, and any content where visual layout and information are crucial. It's particularly effective for documents containing mixed content types like equations, diagrams, charts, and multilingual text.
