Llama-2-7B-GPTQ
| Property | Value |
|---|---|
| Base Model | Meta's Llama-2-7B |
| Parameter Count | 7 Billion |
| License | Llama2 |
| Paper | Research Paper |
| Quantization | GPTQ 4-bit |
What is Llama-2-7B-GPTQ?
Llama-2-7B-GPTQ is a quantized version of Meta's Llama 2 language model, optimized for efficient inference while maintaining performance. This implementation uses GPTQ quantization to reduce the model size and memory requirements while preserving the model's capabilities. The model offers multiple quantization options with different group sizes (32g, 64g, 128g) to balance between performance and resource usage.
Implementation Details
The model utilizes 4-bit quantization with various group sizes and includes options for Act Order optimization. The implementation provides multiple branches with different configurations to suit various hardware setups and performance requirements.
- 4-bit quantization with group sizes 32g, 64g, and 128g
- Compatible with AutoGPTQ, Transformers, and ExLlama
- Supports context length up to 4096 tokens
- Multiple model variations optimized for different use cases
Core Capabilities
- Text generation and completion tasks
- Efficient inference with reduced memory footprint
- Support for both CPU and GPU deployment
- Integration with popular frameworks and libraries
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient quantization implementation, offering multiple compression options while maintaining the core capabilities of the original Llama 2 model. It's particularly notable for its balance between performance and resource efficiency.
Q: What are the recommended use cases?
The model is well-suited for commercial and research applications in English, particularly for text generation tasks. It's ideal for deployments where memory efficiency is crucial while maintaining good performance.
