DeepSeek-R1-Distill-Llama-8B-Abliterated-Q4_K_M-GGUF
| Property | Value |
|---|---|
| Base Architecture | Llama |
| Parameters | 8B |
| Format | GGUF (Q4_K_M quantization) |
| Source | Hugging Face |
What is DeepSeek-R1-Distill-Llama-8B-Abliterated-Q4_K_M-GGUF?
This is a quantized version of the DeepSeek-R1-Distill-Llama-8B model, converted to the efficient GGUF format for use with llama.cpp. The model represents a distilled version of the larger Llama architecture, optimized for better performance while maintaining capability through DeepSeek's distillation process.
Implementation Details
The model utilizes Q4_K_M quantization, which provides an optimal balance between model size and performance. It's specifically designed to work with llama.cpp, enabling efficient inference on both CPU and GPU hardware.
- GGUF format optimization for improved memory efficiency
- Q4_K_M quantization for balanced performance
- Compatible with llama.cpp's server and CLI interfaces
- 2048 context window support
Core Capabilities
- Efficient local inference through llama.cpp integration
- Reduced memory footprint through quantization
- Maintaining base model capabilities despite compression
- Cross-platform support (Linux, MacOS)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient GGUF format implementation and Q4_K_M quantization, making it particularly suitable for local deployment while maintaining good performance characteristics of the original DeepSeek distilled model.
Q: What are the recommended use cases?
The model is ideal for users who need to run inference locally with limited computational resources, particularly through llama.cpp. It's well-suited for applications requiring reasonable performance while maintaining efficiency in memory usage and computation.
