Azure_Dusk-v0.2-i1-GGUF
| Property | Value |
|---|---|
| Original Model | Azure Dusk v0.2 |
| Quantization Types | Multiple (IQ and standard) |
| Size Range | 3.1GB - 10.2GB |
| Source | Hugging Face |
What is Azure_Dusk-v0.2-i1-GGUF?
Azure_Dusk-v0.2-i1-GGUF is a comprehensive collection of quantized versions of the Azure Dusk v0.2 model, offering various compression levels using both imatrix (IQ) and standard quantization techniques. This implementation focuses on providing multiple options for different use-case requirements, from lightweight deployments to higher-quality implementations.
Implementation Details
The model comes in multiple quantization variants, ranging from highly compressed 3.1GB versions to high-quality 10.2GB implementations. It utilizes advanced quantization techniques including IQ (imatrix) quantization, which often provides better quality than traditional quantization at similar file sizes.
- Multiple compression levels (Q2_K to Q6_K)
- IQ variants offering improved quality/size ratio
- Optimized versions for different performance targets
- File sizes ranging from 3.1GB to 10.2GB
Core Capabilities
- Q4_K_M variant (7.6GB) recommended for balanced performance
- IQ3 variants offering better quality than standard Q3_K quantization
- Ultra-lightweight options available (starting at 3.1GB)
- High-quality Q6_K variant for maximum performance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, particularly the implementation of imatrix quantization which often provides better quality than traditional quantization methods at similar file sizes. The variety of options allows users to choose the perfect balance between model size and performance for their specific use case.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (7.6GB) is recommended as it provides an optimal balance of speed and quality. For resource-constrained environments, the IQ3 variants offer good quality at smaller sizes. The Q6_K variant is ideal for users prioritizing quality over file size.
