DeepSeek-R1-Distill-Llama-8B-Uncensored-GGUF
| Property | Value |
|---|---|
| Base Model | DeepSeek-R1-Distill-Llama-8B-Uncensored |
| Format | GGUF |
| Author | mradermacher |
| Model Hub | Hugging Face |
What is DeepSeek-R1-Distill-Llama-8B-Uncensored-GGUF?
This is a quantized version of the DeepSeek-R1-Distill-Llama-8B-Uncensored model, converted into the GGUF format for optimized deployment and reduced storage requirements. The model offers various quantization options to balance between model size and performance, ranging from highly compressed 3.3GB versions to full 16-bit precision at 16.2GB.
Implementation Details
The model implements several quantization strategies, each optimized for different use cases. The quantization options include Q2_K through Q8_0, with special attention to IQ4_XS implementations for enhanced efficiency.
- Multiple quantization options (Q2_K to Q8_0)
- Size ranges from 3.3GB to 16.2GB
- Includes specialized IQ-quants for optimal performance
- Weighted/imatrix quants available in separate repository
Core Capabilities
- Fast inference with Q4_K_S and Q4_K_M variants (recommended)
- High-quality output with Q6_K and Q8_0 quantization
- Optimized storage efficiency with Q2_K and Q3_K variants
- Balance between model size and performance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the optimal balance between model size and performance. The availability of IQ-quants also provides enhanced quality compared to traditional quantization methods.
Q: What are the recommended use cases?
For general usage, the Q4_K_S and Q4_K_M variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, Q8_0 is recommended, while Q2_K and Q3_K variants are suitable for environments with strict storage constraints.
