DeepSeek-R1-Distill-Qwen-14B-abliterated-i1-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Type | GGUF Quantized Language Model |
| Base Model | DeepSeek-R1-Distill-Qwen-14B |
| Hugging Face | Repository Link |
What is DeepSeek-R1-Distill-Qwen-14B-abliterated-i1-GGUF?
This is a specialized quantized version of the DeepSeek-R1-Distill-Qwen-14B model, offering various compression levels through GGUF format. The model provides multiple quantization options ranging from 3.7GB to 12.2GB, allowing users to choose the optimal balance between model size and performance for their specific use case.
Implementation Details
The model implements both weighted and imatrix quantization techniques, offering different compression levels with varying quality-size tradeoffs. The quantization options include IQ (Integer Quantization) variants and standard Q variants, each optimized for specific use cases.
- Multiple quantization levels from IQ1 to Q6_K
- Size options ranging from 3.7GB (i1-IQ1_S) to 12.2GB (i1-Q6_K)
- IQ-quants generally offer better quality than similar-sized non-IQ variants
- Optimal recommendation: Q4_K_M (9.1GB) for balanced performance
Core Capabilities
- Efficient deployment with minimal quality loss through advanced quantization
- Flexible size options for different hardware constraints
- Optimized performance-to-size ratio with IQ variants
- Compatible with standard GGUF loaders and frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, particularly the implementation of both standard and IQ quantization techniques, allowing users to precisely balance their needs between model size and performance quality.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (9.1GB) is recommended as it offers the best balance of speed and quality. For resource-constrained environments, the IQ3 variants provide good quality at smaller sizes, while the Q6_K variant is suitable for users prioritizing quality over size.
