Wizard-Vicuna-30B-Uncensored-GPTQ
| Property | Value |
|---|---|
| Model Size | 30B parameters |
| Quantization | 4-bit GPTQ |
| License | Other |
| Language | English |
What is Wizard-Vicuna-30B-Uncensored-GPTQ?
Wizard-Vicuna-30B-Uncensored-GPTQ is a quantized version of Eric Hartford's original model, optimized for efficient GPU inference while maintaining high performance. This model represents a significant advancement in large language model deployment, offering multiple quantization options to balance between performance and resource requirements.
Implementation Details
The model utilizes GPTQ quantization technology with various parameter configurations, including 4-bit, 3-bit, and 8-bit options. It features different group sizes (32g, 64g, 128g) and Act Order implementations, allowing users to choose the optimal configuration for their specific hardware setup.
- Multiple quantization options ranging from 3-bit to 8-bit precision
- Configurable group sizes for VRAM optimization
- Compatible with AutoGPTQ and text-generation-inference
- Supports various inference frameworks including ExLlama for 4-bit versions
Core Capabilities
- Uncensored text generation without built-in alignment restrictions
- Efficient GPU inference with reduced memory footprint
- Multiple configuration options for different hardware setups
- Comprehensive prompt template support
- Integration with popular frameworks and interfaces
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of large-scale capabilities (30B parameters) with efficient quantization options, allowing for deployment on consumer-grade hardware while maintaining high performance. The uncensored nature allows for custom alignment implementation.
Q: What are the recommended use cases?
The model is suitable for research and development in natural language processing, particularly when unrestricted outputs are needed. It's ideal for applications requiring efficient GPU inference while maintaining high-quality language generation capabilities.
