Kartoffel-Deepfry-12B-GGUF
| Property | Value |
|---|---|
| Base Model Size | 12B Parameters |
| Model Type | GGUF Quantized LLM |
| Author | mradermacher |
| Source | HuggingFace Repository |
What is Kartoffel-Deepfry-12B-GGUF?
Kartoffel-Deepfry-12B-GGUF is a quantized version of the original Kartoffel-Deepfry-12B model, optimized for efficient deployment while maintaining performance. The model offers multiple quantization options ranging from highly compressed (Q2_K at 4.9GB) to high-quality (Q8_0 at 13.1GB) variants, allowing users to choose based on their specific needs for speed, quality, and resource constraints.
Implementation Details
The model implements various quantization techniques, including standard K-quants and specialized IQ-quants. Notable variants include the recommended Q4_K_S (7.2GB) and Q4_K_M (7.6GB) for balanced performance, and Q6_K (10.2GB) for very good quality output.
- Multiple quantization options from Q2 to Q8
- Size variants ranging from 4.9GB to 13.1GB
- IQ-quants available for optimal quality/size ratio
- Weighted/imatrix variants available separately
Core Capabilities
- Efficient deployment with minimal quality loss
- Flexible size options for different hardware constraints
- Optimized performance-to-size ratios
- Compatible with standard GGUF loading frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, allowing users to precisely balance between model size and performance. The availability of both standard and IQ-quants provides additional flexibility for different use cases.
Q: What are the recommended use cases?
For general use, the Q4_K_S and Q4_K_M variants are recommended for their balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K variants.
