L3.1-Athena-a-8B-GGUF
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Author | mradermacher |
| Repository | Hugging Face |
| Format | GGUF (Various Quantizations) |
What is L3.1-Athena-a-8B-GGUF?
L3.1-Athena-a-8B-GGUF is a quantized version of the original L3.1-Athena model, optimized for efficient deployment and reduced memory footprint. This implementation offers multiple quantization options ranging from highly compressed (Q2_K at 3.3GB) to full precision (f16 at 16.2GB), allowing users to balance between model size and performance.
Implementation Details
The model is available in various GGUF quantization formats, each optimized for different use cases. The quantization options include standard variants (Q2-Q8) and specialized formats like IQ4_XS, offering different trade-offs between model size, inference speed, and quality.
- Q2_K: Smallest size at 3.3GB
- Q4_K_S/M: Fast and recommended variants (4.8-5.0GB)
- Q6_K: Very good quality at 6.7GB
- Q8_0: Best quality with fast performance at 8.6GB
- F16: Full precision at 16.2GB
Core Capabilities
- Efficient deployment with multiple compression options
- Optimized inference performance
- Flexible size-quality trade-offs
- Compatible with standard GGUF loaders
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, allowing users to choose the optimal balance between model size and quality for their specific use case. The availability of IQ-quants (Improved Quantization) provides better quality compared to similar-sized traditional quantization methods.
Q: What are the recommended use cases?
For most applications, the Q4_K_S and 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 extremely resource-constrained environments.
