Llama-2-7B-Chat-GGUF
| Property | Value |
|---|---|
| Parameter Count | 6.74B |
| License | Llama2 |
| Paper | arXiv:2307.09288 |
| Author | Meta (Original), TheBloke (GGUF Conversion) |
What is Llama-2-7B-Chat-GGUF?
Llama-2-7B-Chat-GGUF is a converted version of Meta's Llama 2 chat model optimized for efficient inference using the GGUF format. This model represents a significant advancement in accessible AI, offering various quantization options from 2-bit to 8-bit precision to balance performance and resource usage.
Implementation Details
The model utilizes the new GGUF format, which replaces the older GGML format, providing improved tokenization and special token support. It's compatible with multiple platforms and libraries, including llama.cpp, text-generation-webui, and various Python implementations.
- Multiple quantization options (Q2_K through Q8_0) for different performance/quality tradeoffs
- RAM requirements ranging from 5.33GB to 9.66GB depending on quantization
- Supports context length of 4K tokens
- Implements specific prompt template for optimal chat performance
Core Capabilities
- Optimized for dialogue and chat applications
- Balanced performance across various benchmarks including reasoning and knowledge tasks
- Enhanced safety features through supervised fine-tuning and RLHF
- GPU acceleration support with layer offloading capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model combines Meta's powerful Llama 2 architecture with the efficient GGUF format, offering multiple quantization options that make it accessible for various hardware configurations while maintaining good performance.
Q: What are the recommended use cases?
The model is best suited for chat applications, dialogue systems, and interactive AI assistants. The Q4_K_M quantization offers a good balance between model size and performance for most users.
