mxbai-embed-xsmall-v1
| Property | Value |
|---|---|
| Parameter Count | 24.1M |
| License | Apache 2.0 |
| Base Model | sentence-transformers/all-MiniLM-L6-v2 |
| Research Papers | AnglE Paper, Espresso Paper |
What is mxbai-embed-xsmall-v1?
mxbai-embed-xsmall-v1 is an innovative English embedding model developed by Mixedbread AI, built upon the foundation of sentence-transformers/all-MiniLM-L6-v2. This model represents a significant advancement in efficient sentence embedding, incorporating both AnglE loss and Espresso training methodologies to achieve optimal performance while maintaining a compact size.
Implementation Details
The model employs a sophisticated architecture that supports both binary quantization and Matryoshka Representation Learning (MRL). The binary quantization capability allows the model to retain 93.9% of its performance while increasing efficiency by a factor of 32. Additionally, the MRL implementation enables a 33% reduction in vector size while maintaining 96.2% of the model's performance.
- Binary quantization for optimal efficiency
- Matryoshka Representation Learning support
- Average pooling strategy for embedding generation
- FP16 tensor type for balanced precision and efficiency
Core Capabilities
- State-of-the-art sentence embedding generation
- Efficient infrastructure cost reduction through optimization
- Flexible dimension reduction while maintaining performance
- Support for multiple implementation frameworks (angle-emb, sentence-transformers, transformers)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to maintain high performance while offering significant efficiency gains through binary quantization and MRL, making it particularly cost-effective for production deployments.
Q: What are the recommended use cases?
The model is optimized for retrieval tasks and is particularly well-suited for applications requiring efficient sentence embeddings, such as semantic search, document similarity, and information retrieval systems where infrastructure costs are a consideration.
