patricide-12B-Unslop-Mell-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Base Model | redrix/patricide-12B-Unslop-Mell |
| Format | GGUF |
| Size Range | 4.9GB - 13.1GB |
What is patricide-12B-Unslop-Mell-GGUF?
This is a quantized version of the patricide-12B-Unslop-Mell model, optimized for efficient deployment through various GGUF compression formats. It offers multiple quantization options to balance between model size and performance, ranging from lightweight Q2_K (4.9GB) to high-quality Q8_0 (13.1GB) variants.
Implementation Details
The model implements different quantization techniques, including static and weighted/imatrix quantizations. The implementation provides various compression levels optimized for different use cases, with special attention to ARM architecture compatibility and performance optimization.
- Multiple quantization options (Q2 through Q8)
- IQ-quants available for optimal quality-size ratio
- ARM-optimized variants available (Q4_0_4_4)
- Weighted/imatrix quantizations available in separate repository
Core Capabilities
- Fast inference with Q4_K_S and Q4_K_M variants (recommended)
- High-quality output with Q6_K and Q8_0 variants
- Efficient deployment on resource-constrained systems with Q2_K option
- ARM-specific optimizations for mobile/edge deployment
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, allowing users to choose the optimal balance between model size, inference speed, and output quality. It includes special optimizations for ARM architecture and provides both standard and improved IQ-quants.
Q: What are the recommended use cases?
For general use, the Q4_K_S and Q4_K_M variants are recommended as they offer a good balance of speed and quality. For highest quality outputs, use Q8_0, while for resource-constrained environments, the Q2_K variant provides the smallest footprint.
