DeepSeek-R1-Distill-Qwen-14B-Uncensored-i1-GGUF
| Property | Value |
|---|---|
| Base Model | DeepSeek-R1-Distill-Qwen-14B-Uncensored |
| Author | mradermacher |
| Format | GGUF with imatrix quantization |
| Model URL | Hugging Face Repository |
What is DeepSeek-R1-Distill-Qwen-14B-Uncensored-i1-GGUF?
This is a specialized quantized version of the DeepSeek-R1-Distill-Qwen-14B-Uncensored model, optimized for efficient deployment using the GGUF format. The model offers various quantization options, ranging from highly compressed 3.7GB versions to high-quality 12.2GB implementations, providing flexibility for different hardware constraints and use cases.
Implementation Details
The model implements innovative imatrix quantization techniques, offering superior quality compared to traditional quantization methods. It provides multiple quantization variants, each optimized for different size-quality trade-offs.
- Size options range from 3.7GB (IQ1_S) to 12.2GB (Q6_K)
- Features both standard and imatrix-based quantization methods
- Includes optimized variants for different performance requirements
- Implements memory-efficient compression while maintaining model capabilities
Core Capabilities
- Supports various quantization levels (Q2 to Q6)
- Offers imatrix quantization options (IQ) for improved quality
- Provides size-optimized variants for resource-constrained environments
- Maintains model functionality while reducing memory footprint
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its implementation of imatrix quantization techniques, offering better quality than traditional quantization methods at similar file sizes. It provides a wide range of quantization options to balance between model size and performance.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (9.1GB) is recommended as it provides a good balance of speed and quality. For resource-constrained environments, the IQ3 variants offer reasonable performance at smaller sizes. The Q6_K variant (12.2GB) is recommended for highest quality needs.
