Kartoffel-Deepfry-12B-i1-GGUF
Property | Value |
---|---|
Base Model | Kartoffel-Deepfry-12B |
Format | GGUF |
Author | mradermacher |
Size Range | 3.1GB - 10.2GB |
Model Link | Hugging Face |
What is Kartoffel-Deepfry-12B-i1-GGUF?
Kartoffel-Deepfry-12B-i1-GGUF is a comprehensive quantized version of the original Kartoffel-Deepfry-12B model, offering various compression options through weighted/imatrix quantization. This implementation provides multiple variants optimized for different use cases, ranging from highly compressed 3.1GB versions to high-quality 10.2GB implementations.
Implementation Details
The model offers multiple quantization types, including IQ (imatrix) and standard quantization methods. Each variant is carefully balanced between size, speed, and quality, with options ranging from IQ1 to Q6_K compression levels.
- Multiple compression options (IQ1_S through Q6_K)
- Size variants from 3.1GB to 10.2GB
- Optimized imatrix quantization for better quality at smaller sizes
- Various speed/quality trade-offs for different use cases
Core Capabilities
- Efficient deployment with significantly reduced model size
- Optimized performance with IQ-quants often outperforming similar-sized standard quants
- Flexible implementation options based on hardware constraints
- Maintained quality even in smaller size variants through innovative quantization
Frequently Asked Questions
Q: What makes this model unique?
This model implementation stands out for its comprehensive range of quantization options, particularly the imatrix variants that often provide better quality than traditional quantization at similar sizes. The Q4_K_M variant (7.6GB) is specifically recommended for its optimal balance of speed and quality.
Q: What are the recommended use cases?
For production use, the Q4_K_M (7.6GB) variant is recommended for its balance of speed and quality. For resource-constrained environments, the IQ3 variants offer good performance at smaller sizes. The Q6_K variant (10.2GB) is ideal for cases where maximum quality is required.