WizardLM 7B GGML
| Property | Value |
|---|---|
| Author | TheBloke |
| License | Other |
| Format | GGML |
| Size Options | 3.79GB - 7.16GB |
What is wizardLM-7B-GGML?
WizardLM 7B GGML is a specialized implementation of the WizardLM model optimized for CPU and GPU inference using llama.cpp. It offers various quantization options ranging from 4-bit to 8-bit, allowing users to balance between model size, performance, and accuracy based on their requirements.
Implementation Details
The model comes in multiple quantization variants, each optimized for different use cases. The implementation supports both original llama.cpp quantization methods (q4_0, q4_1, q5_0, q5_1, q8_0) and is designed for compatibility with various UI frameworks and libraries.
- Multiple quantization options from 3.79GB (q4_0) to 7.16GB (q8_0)
- Compatible with text-generation-webui, KoboldCpp, and other llama.cpp-based interfaces
- Supports GPU layer offloading for improved performance
- Optimized for both CPU and GPU inference
Core Capabilities
- Efficient local deployment with minimal resource requirements
- Flexible quantization options for different hardware configurations
- Support for context window of 2048 tokens
- Integration with popular inference frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized GGML format implementation, allowing efficient local deployment with various quantization options, making it accessible for users with different hardware capabilities.
Q: What are the recommended use cases?
The model is ideal for local deployment scenarios where efficient CPU/GPU inference is required. The q4_1 quantization offers a good balance between performance and accuracy for most users, while q8_0 provides near float16 quality for those requiring maximum accuracy.
