NemoMix-Unleashed-12B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 12.2B parameters |
| Model Type | Text Generation |
| Quantization | Multiple GGUF variants |
| Author | bartowski |
What is NemoMix-Unleashed-12B-GGUF?
NemoMix-Unleashed-12B-GGUF is a sophisticated quantized language model that offers multiple compression variants optimized for different hardware configurations. Based on MarinaraSpaghetti's NemoMix-Unleashed-12B, it provides various GGUF quantization options ranging from full F16 precision (24.50GB) down to highly compressed IQ2_M (4.44GB) versions.
Implementation Details
The model utilizes llama.cpp release b3600 for quantization and implements imatrix calibration for optimal performance. It offers multiple quantization types including K-quants and I-quants, each optimized for specific use cases and hardware configurations.
- Multiple quantization options from Q8_0 to IQ2_M
- Special versions with Q8_0 embed/output weights for enhanced quality
- Compatible with LM Studio and various inference engines
- Optimized for different hardware: CPU, GPU (CUDA/ROCm), and Apple Metal
Core Capabilities
- Flexible deployment options for various hardware configurations
- High-quality text generation with adjustable quality-size tradeoffs
- Optimized performance through advanced quantization techniques
- Support for multiple inference backends including cuBLAS and rocBLAS
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its comprehensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. It also implements advanced techniques like specialized embed/output weight quantization for enhanced quality.
Q: What are the recommended use cases?
For maximum quality, users with combined system RAM and GPU VRAM should choose larger variants like Q6_K_L or Q5_K_L. For faster performance on limited hardware, the I-quant variants (IQ4_XS, IQ3_M) offer excellent compression while maintaining reasonable quality. The Q4_K_M variant is recommended as a balanced option for most use cases.
