Zephyr-7B-Beta GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| Base Model | Mistral-7B |
| License | MIT |
| Paper | arxiv:2310.16944 |
| Training Method | Direct Preference Optimization (DPO) |
What is zephyr-7B-beta-GGUF?
Zephyr-7B-Beta GGUF is a quantized version of the high-performing Zephyr language model, converted to the efficient GGUF format by TheBloke. It represents a significant advancement in accessible AI, offering various quantization levels for different performance needs while maintaining impressive capabilities.
Implementation Details
The model is based on Mistral-7B and has been fine-tuned using Direct Preference Optimization on the UltraFeedback dataset. It achieves remarkable performance, scoring 7.34 on MT-Bench, surpassing many larger models.
- Multiple quantization options from 2-bit to 8-bit (Q2_K to Q8_0)
- Optimized for both CPU and GPU inference
- Compatible with popular frameworks like llama.cpp and text-generation-webui
Core Capabilities
- Strong performance in chat and instruction-following tasks
- Competitive MT-Bench scores (7.34) surpassing some 70B parameter models
- Efficient memory usage with various quantization options
- Excellent performance in general knowledge and reasoning tasks
Frequently Asked Questions
Q: What makes this model unique?
Its exceptional performance-to-size ratio and variety of quantization options make it highly accessible while maintaining strong capabilities comparable to much larger models.
Q: What are the recommended use cases?
The model excels in chat applications, instruction following, and general knowledge tasks. The various quantization options allow deployment on different hardware configurations, from resource-constrained environments to high-performance systems.
