WizardLM-7B-GPTQ-4bit-128g
| Property | Value |
|---|---|
| Base Model | WizardLM-7B |
| Quantization | 4-bit GPTQ |
| Group Size | 128 |
| Source | Original Repository |
What is wizardLM-7B-GPTQ-4bit-128g?
WizardLM-7B-GPTQ-4bit-128g is a quantized version of the WizardLM language model, specifically optimized for efficient deployment while maintaining performance. This version uses GPTQ quantization with 4-bit precision and a group size of 128, significantly reducing the model's memory footprint while preserving most of its capabilities.
Implementation Details
The model implements GPTQ quantization, a sophisticated compression technique that reduces the model's precision to 4 bits while maintaining performance through careful quantization strategies. The 128g designation refers to the group size used in quantization, which helps balance compression and accuracy.
- 4-bit quantization for efficient memory usage
- 128 group size for balanced performance
- Derived from the original WizardLM-7B model
- Optimized for deployment on consumer hardware
Core Capabilities
- Maintains most of the original model's language understanding abilities
- Reduced memory footprint compared to full-precision model
- Suitable for running on consumer-grade GPUs
- Compatible with standard transformer-based architectures
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient quantization that makes the powerful WizardLM-7B accessible on consumer hardware while maintaining good performance. The 4-bit precision with 128 group size represents a sweet spot between compression and quality.
Q: What are the recommended use cases?
This model is ideal for developers and researchers who need to deploy large language models with limited computational resources. It's particularly suitable for applications requiring good language understanding capabilities while operating within memory constraints.
