LLM-Embedder
| Property | Value |
|---|---|
| Parameter Count | 109M parameters |
| License | MIT |
| Author | BAAI |
| Paper | Research Paper |
| Framework | PyTorch, Transformers |
What is llm-embedder?
LLM-Embedder is a state-of-the-art text embedding model designed specifically for Large Language Model (LLM) retrieval augmentation. It maps text to low-dimensional dense vectors, enabling efficient semantic search, classification, and clustering tasks. The model represents a significant advancement in unified embedding approaches for diverse retrieval needs.
Implementation Details
Built on the FlagEmbedding framework, LLM-Embedder utilizes advanced transformer architecture with 109M parameters. It supports both PyTorch and Safetensors formats, offering flexible deployment options. The model implements sophisticated text-embeddings-inference techniques and provides dedicated inference endpoints for production use.
- Optimized for both English and Chinese text embedding
- Supports variable sequence lengths with efficient processing
- Implements contrastive learning with temperature-controlled similarity distribution
- Features built-in instruction handling for improved retrieval performance
Core Capabilities
- Generate high-quality dense vector representations
- Support for semantic search and document retrieval
- Cross-lingual embedding capabilities
- Efficient integration with vector databases
- Flexible API support through multiple frameworks
Frequently Asked Questions
Q: What makes this model unique?
LLM-Embedder stands out for its unified approach to embedding generation, specifically optimized for LLM retrieval augmentation. It achieves state-of-the-art performance on both MTEB and C-MTEB benchmarks while maintaining efficient computational requirements.
Q: What are the recommended use cases?
The model excels in semantic search, document retrieval, text classification, and clustering tasks. It's particularly well-suited for building retrieval-augmented LLM systems and maintaining vector databases for advanced language processing applications.
