Llama-2-7B-Chat-GGML
| Property | Value |
|---|---|
| Base Model | Meta Llama 2 7B Chat |
| Architecture | Transformer-based LLM |
| License | Meta Custom License |
| Paper | arxiv:2307.09288 |
| Context Length | 4096 tokens |
What is Llama-2-7B-Chat-GGML?
Llama-2-7B-Chat-GGML is a quantized version of Meta's Llama 2 chat model, specifically optimized for CPU and GPU inference using the GGML format. This model provides multiple quantization options ranging from 2-bit to 8-bit precision, allowing users to balance between model size, performance, and resource usage. Created by TheBloke, it's designed to run efficiently on consumer hardware while maintaining good performance.
Implementation Details
The model comes in various quantization formats, from lightweight 2-bit versions (2.87GB) to high-precision 8-bit versions (7.16GB). It uses advanced k-quant methods and supports different quantization configurations for different tensor types. The implementation is compatible with multiple frameworks including llama.cpp, text-generation-webui, and KoboldCpp.
- Multiple quantization options (q2_K through q8_0)
- GPU acceleration support
- 4096 token context window
- Optimized for dialogue use cases
Core Capabilities
- Chat-style interactions with proper prompt formatting
- General knowledge and reasoning tasks
- Safety-aligned responses
- Multiple inference options through various front-ends
- Efficient resource utilization through quantization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its versatile quantization options that make it accessible for different hardware configurations while preserving the core capabilities of Llama 2. It's specifically optimized for chat applications and includes safety considerations in its responses.
Q: What are the recommended use cases?
The model is best suited for chat applications, general text generation, and assistant-like interactions. It can be deployed in scenarios where balanced performance and resource usage are important, with different quantization options available based on specific needs.
