WizardLM-2-7B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| Base Model | Mistral-7B-v0.1 |
| License | Apache 2.0 |
| Developer | Microsoft AI |
| Format | GGUF (Various Quantizations) |
What is WizardLM-2-7B-GGUF?
WizardLM-2-7B-GGUF is a quantized version of Microsoft's WizardLM-2 language model, optimized for efficient deployment while maintaining high performance. Built on the Mistral-7B architecture, this model represents a significant advancement in compact yet powerful language models, capable of handling complex chat interactions, multilingual tasks, and sophisticated reasoning.
Implementation Details
The model utilizes the GGUF format, offering various quantization levels (2-bit to 8-bit) for flexible deployment options. It features a sophisticated prompt template system and supports multi-turn conversations following the Vicuna format. The model was trained using a fully AI-powered synthetic training system, enabling enhanced performance across diverse tasks.
- Multiple quantization options from 2-bit to 8-bit precision
- Supports context lengths up to 32K tokens
- Implements advanced prompt formatting for optimal interaction
- GPU-accelerated inference capability
Core Capabilities
- Competitive performance on MT-Bench evaluation framework
- Strong multilingual support
- Advanced reasoning and complex chat capabilities
- Comparable performance to models 10x larger in size
- Efficient deployment options through various quantization levels
Frequently Asked Questions
Q: What makes this model unique?
WizardLM-2-7B-GGUF stands out for its ability to achieve performance comparable to much larger models while maintaining efficiency through quantization. It excels in complex chat scenarios and multilingual tasks, making it particularly versatile for various applications.
Q: What are the recommended use cases?
The model is well-suited for complex chat applications, multilingual processing, reasoning tasks, and AI assistance scenarios. It's particularly effective for deployments where balance between performance and resource efficiency is crucial.
