DeepSeek-R1-Distill-Qwen-14B-GGUF
| Property | Value |
|---|---|
| Base Model | DeepSeek-R1-Distill-Qwen-14B |
| Quantization | Multiple GGUF formats |
| Model URL | huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF |
| Author | bartowski |
What is DeepSeek-R1-Distill-Qwen-14B-GGUF?
DeepSeek-R1-Distill-Qwen-14B-GGUF is a comprehensive collection of GGUF quantized versions of the DeepSeek-R1-Distill-Qwen-14B model, offering various compression options to suit different hardware capabilities and use cases. The quantizations range from full precision (F32) at 59.09GB to highly compressed versions (IQ2_XS) at just 4.70GB.
Implementation Details
The model uses llama.cpp's advanced quantization techniques, including imatrix optimization and specialized formats for different hardware architectures. The implementation includes special considerations for embed/output weights and online repacking capabilities for ARM and AVX systems.
- Multiple quantization options from F32 to IQ2
- Special K-L variants with Q8_0 for embed and output weights
- Optimized formats for ARM and AVX systems
- Custom prompt format with system prompts support
Core Capabilities
- Flexible deployment options for various hardware configurations
- Optimized performance through specialized weight layouts
- Support for both high-quality (Q8_0, Q6_K) and space-efficient (IQ2, IQ3) implementations
- Automatic weight repacking for improved performance on ARM/AVX systems
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options and specialized optimizations for different hardware architectures. The implementation includes cutting-edge techniques like online repacking and imatrix quantization, making it highly versatile for various deployment scenarios.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q8_0 variants. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained environments, IQ4_XS offers good quality while maintaining a smaller footprint. The choice depends on available RAM/VRAM and whether you're using CPU, CUDA, or Metal acceleration.
