LLM2Vec-Mistral-7B-Instruct-v2-mntp
| Property | Value |
|---|---|
| License | MIT |
| Paper | View Paper |
| Language | English |
| Framework | Transformers |
What is LLM2Vec-Mistral-7B-Instruct-v2-mntp?
LLM2Vec-Mistral is an innovative text encoder that transforms decoder-only Large Language Models into powerful text embedding generators. Built on the Mistral-7B architecture, it implements a unique three-step process: enabling bidirectional attention, masked next token prediction, and unsupervised contrastive learning to create high-quality text representations.
Implementation Details
The model leverages a sophisticated architecture that combines the power of Mistral-7B with custom code enabling bidirectional connections in decoder-only LLMs. It utilizes bfloat16 precision and supports both CPU and CUDA execution, making it versatile for various deployment scenarios.
- Bidirectional attention mechanism for enhanced context understanding
- Masked Next Token Prediction (MNTP) for improved representation learning
- Flexible pooling operations with customizable maximum sequence length
- Support for instruction-based encoding queries
Core Capabilities
- Text embedding generation for similarity comparison
- Information retrieval and passage matching
- Semantic similarity computation
- Text classification and clustering
- Document and query representation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through its innovative approach to converting decoder-only LLMs into effective text encoders, achieving state-of-the-art performance through its three-step process and ability to handle both instructed and non-instructed inputs.
Q: What are the recommended use cases?
The model excels in tasks such as semantic search, document retrieval, text similarity analysis, and information retrieval applications. It's particularly effective for applications requiring high-quality text embeddings and semantic understanding.
