DeepSeek-R1-Distill-Qwen-7B-abliterated-v2-GGUF
| Property | Value |
|---|---|
| Original Model | huihui-ai/DeepSeek-R1-Distill-Qwen-7B-abliterated-v2 |
| Author | Melvin56 |
| Model Format | GGUF |
| Repository | Hugging Face |
What is DeepSeek-R1-Distill-Qwen-7B-abliterated-v2-GGUF?
This is a quantized version of the DeepSeek-R1-Distill-Qwen-7B model, optimized for efficient deployment using the GGUF format. The model offers various quantization options to balance between model size and performance, ranging from 2.82GB to 15.24GB.
Implementation Details
The model has been quantized using the imatrix option, providing multiple compression levels:
- Q2_K_S: 2.82GB (Highest compression)
- Q3_K_M: 3.80GB
- Q4_0: 4.43GB
- Q5_K_M: 5.44GB
- Q8_0: 8.10GB
- F16: 15.24GB (Highest precision)
Core Capabilities
- Efficient memory usage through various quantization options
- Compatible with llama.cpp framework
- Maintains model functionality while reducing size
- Flexible deployment options based on hardware constraints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its variety of quantization options, allowing users to choose the optimal balance between model size and performance for their specific use case. The implementation uses the imatrix option for quantization, ensuring quality preservation while reducing size.
Q: What are the recommended use cases?
The model is ideal for deployment scenarios where resource efficiency is crucial. The various quantization options make it suitable for different hardware configurations, from resource-constrained environments (using Q2_K_S) to high-performance systems (using F16).
