LLaMA-2-7B-32K
| Property | Value |
|---|---|
| Base Model | LLaMA-2 7B |
| Context Length | 32,000 tokens |
| License | LLaMA 2 |
| Primary Language | English |
| Framework | PyTorch/Transformers |
What is LLaMA-2-7B-32K?
LLaMA-2-7B-32K is an enhanced version of Meta's LLaMA-2 model, developed by Together Computer to handle significantly longer context lengths. This model represents a major advancement in context handling, extending the original model's capabilities from standard context lengths to an impressive 32,000 tokens, making it particularly suitable for long-form text processing tasks.
Implementation Details
The model utilizes position interpolation and incorporates FlashAttention-2 for optimal performance. It underwent a two-phase training process: initial pre-training with a carefully curated mix of long-form content, followed by fine-tuning focused on few-shot learning capabilities.
- Implements position interpolation for extended context handling
- Utilizes FlashAttention-2 for improved efficiency
- Trained on a diverse dataset including RedPajama Book, ArXiv, and UL2 Oscar Data
- Optimized for both inference and training with 32K context windows
Core Capabilities
- Long-form document question-answering
- Book and chapter summarization
- Multi-document analysis
- Extended context comprehension
- Few-shot learning with long contexts
Frequently Asked Questions
Q: What makes this model unique?
This model's standout feature is its ability to process 32K token contexts while maintaining performance, achieved through specialized training and architectural optimizations. It's particularly notable for handling long-form content while preserving the base LLaMA-2 capabilities.
Q: What are the recommended use cases?
The model excels at tasks requiring long context understanding, such as multi-document QA, book summarization, and academic paper analysis. It's especially suitable for applications where maintaining context over long passages is crucial.
