DeepSeek-R1-Distill-Qwen-7B-Uncensored-GGUF
| Property | Value |
|---|---|
| Base Model | DeepSeek-R1-Distill-Qwen-7B-Uncensored |
| Format | GGUF |
| Author | mradermacher |
| Model Hub | Hugging Face |
What is DeepSeek-R1-Distill-Qwen-7B-Uncensored-GGUF?
This is a GGUF-formatted version of the DeepSeek-R1-Distill-Qwen-7B-Uncensored model, offering various quantization options to optimize the trade-off between model size and performance. The model provides multiple quantization variants ranging from 3.1GB to 15.3GB, making it adaptable to different hardware configurations and use cases.
Implementation Details
The model comes in multiple quantization versions, each optimized for different scenarios:
- Q2_K: Smallest size at 3.1GB
- Q4_K_S/M: Recommended versions (4.6-4.8GB) balancing speed and quality
- Q6_K: Very good quality at 6.4GB
- Q8_0: Highest quality quantized version at 8.2GB
- F16: Full precision at 15.3GB
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Optimized performance with IQ-quants variants
- Flexible size options from 3.1GB to 15.3GB
- Fast inference with recommended Q4_K variants
Frequently Asked Questions
Q: What makes this model unique?
This model offers a comprehensive range of quantization options, making it highly versatile for different deployment scenarios. The availability of IQ-quants makes it particularly efficient for specific use cases where quality and size need to be balanced.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants (4.6-4.8GB) are recommended as they offer a good balance of speed and quality. For highest quality requirements, Q8_0 is recommended, while Q2_K can be used where minimal size is crucial.
