Magnum v4 12B GGUF
| Property | Value |
|---|---|
| Original Model | magnum-v4-12b |
| Quantization Types | Multiple (F16 to IQ2) |
| Size Range | 4.14GB - 24.50GB |
| Author | bartowski |
What is magnum-v4-12b-GGUF?
Magnum v4 12B GGUF is a comprehensive collection of quantized versions of the original Magnum v4 12B model, optimized for different hardware configurations and use cases. These quantizations were created using llama.cpp release b3930 with imatrix options, offering various compression levels while maintaining different quality-performance tradeoffs.
Implementation Details
The model offers multiple quantization formats ranging from full F16 weights (24.50GB) to highly compressed IQ2_S (4.14GB) variants. Each quantization type is optimized for specific use cases, with special attention paid to embedding and output weight handling in certain variants.
- Advanced quantization techniques including K-quants and I-quants
- Special ARM-optimized versions (Q4_0_X_X series)
- Embedding/output weight optimization in XL and L variants
- SOTA compression techniques in IQ series maintaining usability
Core Capabilities
- Flexible deployment options for various hardware configurations
- Optimized performance for different RAM/VRAM constraints
- Special optimizations for ARM processors
- Support for multiple inference backends (cuBLAS, rocBLAS, CPU)
Frequently Asked Questions
Q: What makes this model unique?
This model provides an extensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. The implementation includes cutting-edge quantization techniques and special optimizations for various hardware architectures.
Q: What are the recommended use cases?
For maximum performance, choose a variant that fits within your GPU's VRAM with 1-2GB headroom. For maximum quality, select a variant that fits within your combined system RAM and GPU VRAM. K-quants (Q4_K_M, Q5_K_M) are recommended for general use, while I-quants are better for lower quantization levels on specific hardware.
