Selene-1-Mini-Llama-3.1-8B-GGUF
| Property | Value |
|---|---|
| Base Model | Selene-1-Mini-Llama-3.1-8B |
| Model Size | 8B parameters |
| Format | GGUF (Various quantizations) |
| Author | mradermacher |
| Source | Hugging Face |
What is Selene-1-Mini-Llama-3.1-8B-GGUF?
This is a quantized version of the Selene-1-Mini-Llama model, specifically optimized for efficient deployment through the GGUF format. It offers multiple quantization options ranging from 3.3GB to 16.2GB, allowing users to balance between model size and performance quality.
Implementation Details
The model provides various quantization types, each optimized for different use cases. Notable variants include Q4_K_S and Q4_K_M (recommended for general use), Q6_K for very good quality, and Q8_0 for best quality at larger size. The implementation includes both standard and IQ (improved quantization) variants.
- Q4_K_M (5.0GB): Fast and recommended for general use
- Q6_K (6.7GB): Very good quality with moderate size
- Q8_0 (8.6GB): Highest quality option for critical applications
- F16 (16.2GB): Full precision, typically overkill for most uses
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Optimized performance-to-size ratios
- Compatible with standard GGUF loading tools
- Supports both standard and improved quantization (IQ) variants
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 quality for their specific use case. The inclusion of both standard and IQ quantization provides additional flexibility.
Q: What are the recommended use cases?
For most applications, the Q4_K_M variant is recommended as it provides a good balance of speed and quality. For applications requiring higher accuracy, Q6_K or Q8_0 variants are suggested, while more resource-constrained environments might benefit from the smaller Q2_K or Q3_K_S variants.
