WizardLM-30B-Uncensored-GPTQ
| Property | Value |
|---|---|
| Parameter Count | 4.45B (Quantized) |
| Model Type | LLaMA Architecture |
| License | Other |
| Author | TheBloke (Quantized) / Eric Hartford (Original) |
What is WizardLM-30B-Uncensored-GPTQ?
WizardLM-30B-Uncensored-GPTQ is a quantized version of Eric Hartford's WizardLM model, specifically designed to run efficiently on consumer hardware while maintaining high performance. This version has been trained without typical AI alignment constraints, allowing for more flexible deployment with custom alignment solutions.
Implementation Details
The model offers multiple GPTQ quantization options, ranging from 3-bit to 8-bit precision, with various group sizes (32g, 64g, 128g) and Act Order configurations. The 4-bit versions are particularly notable for their balance of performance and VRAM efficiency.
- Multiple quantization options from 3-bit to 8-bit precision
- Group size variations for VRAM optimization
- Act Order configurations for improved accuracy
- Compatible with ExLlama for 4-bit versions
Core Capabilities
- High-quality text generation with reduced VRAM requirements
- Flexible deployment options with different precision levels
- Optimized for both consumer and professional GPU usage
- Support for standard transformer-based text generation tasks
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its uncensored training approach and variety of quantization options, allowing users to choose the optimal balance between performance and resource usage. It's specifically designed to run on consumer hardware while maintaining high-quality output.
Q: What are the recommended use cases?
The model is suitable for general text generation tasks, research purposes, and applications where custom alignment is desired. Users should note that as an uncensored model, it requires careful deployment and appropriate safeguards.
