Magnum-v4-Cydonia-v1.2-22B-i1-GGUF
| Property | Value |
|---|---|
| Parameter Count | 22.2B |
| License | MRL |
| Base Model | knifeayumu/Magnum-v4-Cydonia-v1.2-22B |
| Quantized By | mradermacher |
What is Magnum-v4-Cydonia-v1.2-22B-i1-GGUF?
This is an advanced quantized version of the Magnum-v4-Cydonia model, specifically optimized using iMatrix quantization techniques. The model offers multiple quantization levels ranging from 4.9GB to 18.4GB, providing flexible deployment options based on hardware constraints and performance requirements.
Implementation Details
The model implements various quantization strategies, including IQ (Improved Quantization) variants and traditional quantization methods. It features specialized formats like IQ1, IQ2, IQ3, and IQ4, each offering different trade-offs between model size and performance.
- Multiple quantization options ranging from IQ1_S (4.9GB) to Q6_K (18.4GB)
- Optimized iMatrix quantization for improved performance
- Compatible with standard GGUF implementations
- Includes both speed-optimized and quality-optimized variants
Core Capabilities
- Efficient deployment options for various hardware configurations
- Optimized for conversational tasks
- Maintains model quality while reducing size significantly
- Supports both high-speed and high-quality inference modes
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its comprehensive range of quantization options using iMatrix technology, allowing users to choose the optimal balance between model size and performance. The IQ variants often provide better quality than traditional quantization methods of similar sizes.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (13.4GB) is recommended for general use, offering a good balance of speed and quality. For resource-constrained environments, the IQ3_S variant (9.8GB) provides decent performance while maintaining reasonable quality.
