LLM2Vec-Meta-Llama-3-8B-Instruct-mntp
| Property | Value |
|---|---|
| License | MIT |
| Paper | Research Paper |
| Base Model | Llama 3 8B |
| Primary Use | Text Embedding & Sentence Similarity |
What is LLM2Vec-Meta-Llama-3-8B-Instruct-mntp?
LLM2Vec is an innovative approach that transforms decoder-only language models into powerful text encoders. Built on Meta's Llama 3 architecture, this model implements a three-step process: enabling bidirectional attention, masked next token prediction (MNTP), and unsupervised contrastive learning. It's specifically designed for generating high-quality text embeddings and performing semantic similarity tasks.
Implementation Details
The model utilizes a custom implementation that enables bidirectional connections in decoder-only LLMs, fundamentally changing how the model processes text. It employs the PEFT (Parameter-Efficient Fine-Tuning) framework and supports both CPU and CUDA execution. The model accepts a maximum sequence length of 512 tokens and uses bfloat16 precision for efficient computation.
- Bidirectional attention mechanism for enhanced context understanding
- Masked Next Token Prediction (MNTP) for robust feature learning
- Support for instruction-based encoding
- Flexible pooling strategies with mean pooling as default
Core Capabilities
- Text embedding generation for information retrieval
- Semantic similarity computation between texts
- Support for both query and document encoding
- Fine-tuned performance for BEIR benchmark tasks
- Efficient processing of both instructed and non-instructed inputs
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its ability to convert decoder-only LLMs into effective text encoders through a simple yet powerful three-step process, making it particularly effective for semantic similarity and information retrieval tasks.
Q: What are the recommended use cases?
The model excels in text embedding generation, semantic similarity computation, information retrieval, and text classification tasks. It's particularly well-suited for applications requiring high-quality text representations like search systems and content recommendation.
