Llama-2-7B-32K-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 6.74B parameters |
| Context Length | 32K tokens |
| License | Llama2 |
| Base Model | Together's Llama-2-7B-32K-Instruct |
| Research Paper | Link |
What is Llama-2-7B-32K-Instruct-GGUF?
This is a GGUF-formatted version of Together's Llama-2-7B-32K-Instruct model, specifically optimized for efficient inference across various computing environments. The model represents a significant advancement in long-context language models, capable of processing sequences up to 32,000 tokens while maintaining high performance in instruction-following tasks.
Implementation Details
The model is available in multiple quantization formats, from 2-bit to 8-bit precision, offering different trade-offs between model size and performance. The Q4_K_M variant (4-bit) is recommended for balanced performance, requiring only 6.58GB of RAM.
- Multiple quantization options from Q2_K (2.83GB) to Q8_0 (7.16GB)
- GPU layer offloading support for optimized performance
- Implements improved GGUF format with better tokenization and special token support
- Compatible with major frameworks including llama.cpp, text-generation-webui, and LangChain
Core Capabilities
- Extended context processing up to 32K tokens
- Instruction-following and chat functionality
- High performance on summarization tasks (0.336 R1 score)
- Strong performance on multi-document QA (0.622 accuracy on 20 docs)
Frequently Asked Questions
Q: What makes this model unique?
This model combines the benefits of extended context length (32K tokens) with efficient GGUF formatting and multiple quantization options, making it highly versatile for different deployment scenarios while maintaining strong performance.
Q: What are the recommended use cases?
The model excels at long-form content processing, including document summarization, multi-document question answering, and instruction-following tasks. It's particularly suitable for applications requiring processing of lengthy documents or conversations.
