Cygnus-II-14B-i1-GGUF
| Property | Value |
|---|---|
| Original Model | Cygnus-II-14B |
| Quantization Types | iMatrix and Static |
| Size Range | 3.7GB - 12.2GB |
| Author | mradermacher |
| Model URL | Hugging Face Repository |
What is Cygnus-II-14B-i1-GGUF?
Cygnus-II-14B-i1-GGUF is a comprehensive collection of quantized versions of the Cygnus-II-14B language model, offering various compression levels through both imatrix and static quantization techniques. This implementation focuses on providing multiple options for balancing model size, performance, and quality requirements.
Implementation Details
The model comes in multiple quantization variants, ranging from highly compressed 3.7GB versions to higher-quality 12.2GB implementations. Notable options include IQ-quants (often preferred over similar-sized non-IQ quants) and traditional quantization methods.
- Smallest variant (i1-IQ1_S): 3.7GB - Suitable for resource-constrained environments
- Recommended variant (i1-Q4_K_M): 9.1GB - Optimal balance of speed and quality
- Highest quality variant (i1-Q6_K): 12.2GB - Comparable to static Q6_K quality
Core Capabilities
- Multiple compression options for different use cases
- IQ-quant variants offering better quality at similar sizes
- Optimized performance-to-size ratios
- Compatible with standard GGUF loading systems
Frequently Asked Questions
Q: What makes this model unique?
This implementation stands out for its comprehensive range of quantization options, particularly the inclusion of imatrix variants that often outperform traditional quantization at similar sizes.
Q: What are the recommended use cases?
For optimal performance, the i1-Q4_K_M (9.1GB) variant is recommended as it provides a good balance of speed and quality. For resource-constrained systems, the IQ2 and IQ3 variants offer reasonable performance at smaller sizes.
