MN-Halide-12b-v1.0-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Original Model | Azazelle/MN-Halide-12b-v1.0 |
| Model Format | GGUF (Various Quantizations) |
| Size Range | 4.9GB - 13.1GB |
What is MN-Halide-12b-v1.0-GGUF?
MN-Halide-12b-v1.0-GGUF is a comprehensive collection of quantized versions of the original MN-Halide-12b model, optimized for efficient deployment while maintaining different balance points between model size and quality. This implementation provides various quantization options to suit different hardware and performance requirements.
Implementation Details
The model comes in multiple quantization variants, each optimized for different use cases:
- Q2_K: Smallest size at 4.9GB for minimal resource requirements
- Q4_K_S/M: Fast and recommended variants (7.2GB/7.6GB)
- Q6_K: Very good quality at 10.2GB
- Q8_0: Best quality option at 13.1GB
- Various IQ (Improved Quantization) variants available for better performance
Core Capabilities
- Multiple quantization options for flexible deployment
- Optimized performance-to-size ratios
- Support for various GGUF implementations
- Enhanced efficiency through improved quantization techniques
Frequently Asked Questions
Q: What makes this model unique?
The model offers a comprehensive range of quantization options, allowing users to choose the optimal balance between model size and performance. The inclusion of IQ-quants often provides better performance than similar-sized traditional quantization methods.
Q: What are the recommended use cases?
For optimal performance with reasonable size requirements, the Q4_K_S and Q4_K_M variants are recommended. For highest quality requirements, the Q8_0 variant is suggested, while resource-constrained environments might benefit from the smaller Q2_K or IQ3 variants.
