mmE5-mllama-11b-instruct
| Property | Value |
|---|---|
| Model Size | 11B parameters |
| Base Architecture | Llama-3.2-11B-Vision |
| Paper | arXiv:2502.08468 |
| Author | intfloat |
What is mmE5-mllama-11b-instruct?
mmE5-mllama-11b-instruct is a state-of-the-art multimodal multilingual embedding model that leverages high-quality synthetic data to improve vision-language understanding. Built upon Llama-3.2-11B-Vision architecture, it achieves superior performance on the MMEB benchmark through innovative training approaches.
Implementation Details
The model utilizes a sophisticated architecture combining vision and language processing capabilities, trained on carefully curated datasets including mmE5-MMEB-hardneg and mmE5-synthetic. It supports both image-to-text and text-to-image matching functionalities, implementing advanced pooling and normalization techniques for embedding generation.
- Supports both Transformers and Sentence Transformers implementations
- Implements last-pooling with normalization for representation
- Provides efficient similarity computation between embeddings
- Handles multimodal inputs seamlessly
Core Capabilities
- Multimodal embedding generation for images and text
- Cross-modal similarity matching
- Multilingual support
- State-of-the-art performance on MMEB benchmark
- Flexible API support through multiple frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its use of high-quality synthetic data for training and its ability to generate effective multimodal multilingual embeddings, achieving SOTA performance on the MMEB benchmark. It's built on the robust Llama-3.2-11B-Vision architecture and supports both image-to-text and text-to-image matching.
Q: What are the recommended use cases?
The model is ideal for applications requiring cross-modal similarity matching, multimodal search, image-text alignment, and multilingual content understanding. It's particularly effective for tasks involving image and text comparison, retrieval, and semantic similarity analysis.
