Zephyr-7B-beta-AWQ
| Property | Value |
|---|---|
| Parameter Count | 7 billion |
| Model Type | Mistral-based chat model |
| License | MIT |
| Research Paper | Zephyr: Direct Distillation of LM Alignment |
| Quantization | 4-bit AWQ |
What is zephyr-7B-beta-AWQ?
Zephyr-7B-beta-AWQ is a quantized version of the Zephyr language model, optimized using Advanced Weight Quantization (AWQ) technique. Built on Mistral-7B architecture, this model has been fine-tuned on the UltraChat dataset and further aligned using Direct Preference Optimization (DPO) on the UltraFeedback dataset. The model achieves remarkable performance, scoring 7.34 on MT-Bench, surpassing many larger models.
Implementation Details
The model uses 4-bit precision quantization through AWQ, reducing the model size while maintaining performance. It's compatible with various frameworks including text-generation-webui, vLLM, and Hugging Face's Text Generation Inference.
- Base Model: Mistral-7B-v0.1
- Training Datasets: UltraChat and UltraFeedback
- Quantization Method: AWQ (4-bit)
- Model Size: 4.15GB after quantization
Core Capabilities
- High-performance chat and text generation
- Strong performance on MT-Bench (7.34 score)
- Efficient inference with reduced memory footprint
- Compatible with multiple deployment frameworks
- Supports context length of 4096 tokens
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of AWQ quantization with strong performance metrics, achieving better results than many larger models while maintaining a smaller footprint. It's particularly notable for matching or exceeding the performance of 70B parameter models in certain tasks.
Q: What are the recommended use cases?
The model is best suited for chat applications, general text generation, and tasks requiring strong language understanding. However, it should be noted that it may have limitations in complex tasks like coding and mathematics compared to larger proprietary models.
