DeepSeek-R1-Distill-Llama-8B-Abliterated-GGUF
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Author | mradermacher |
| Format | GGUF |
| Source Model | DeepSeek-R1-Distill-Llama-8B-Abliterated |
What is DeepSeek-R1-Distill-Llama-8B-Abliterated-GGUF?
This model is a quantized version of the DeepSeek-R1-Distill-Llama-8B model, converted to the GGUF format for efficient deployment. It offers multiple quantization options ranging from 3.3GB to 16.2GB, allowing users to balance between model size and performance based on their requirements.
Implementation Details
The model provides various quantization types, with each offering different tradeoffs between size and quality. Notable implementations include Q4_K_S and Q4_K_M which are recommended for their balance of speed and quality, Q6_K for very good quality, and Q8_0 for the best quality while maintaining reasonable size.
- Q2_K: Smallest size at 3.3GB
- Q4_K_S/M: Fast and recommended (4.8-5.0GB)
- Q6_K: Very good quality at 6.7GB
- Q8_0: Best quality at 8.6GB
- F16: Full precision at 16.2GB
Core Capabilities
- Multiple quantization options for different use cases
- Optimized for different hardware configurations
- Maintains model quality while reducing size
- Compatible with standard GGUF loaders
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its variety of quantization options, allowing users to choose the perfect balance between model size and performance. The availability of IQ-quants (like IQ4_XS) provides additional optimization options often preferable over similar-sized non-IQ quants.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or 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 is suitable for resource-constrained environments.
