EtherealAurora-12B-i1-GGUF
| Property | Value |
|---|---|
| Base Model | EtherealAurora-12B |
| Original Source | yamatazen/EtherealAurora-12B |
| Parameters | 12 Billion |
| Format | GGUF with imatrix quantization |
| Author | mradermacher |
What is EtherealAurora-12B-i1-GGUF?
EtherealAurora-12B-i1-GGUF is a quantized version of the EtherealAurora language model, optimized for efficient deployment while maintaining performance. This implementation features various quantization levels, from lightweight 3.1GB versions to high-quality 10.2GB variants, each offering different tradeoffs between model size and performance.
Implementation Details
The model utilizes imatrix quantization techniques to create multiple variants optimized for different use cases. The quantization types range from IQ1_S to Q6_K, with file sizes varying from 3.1GB to 10.2GB. The implementation includes both standard and improved quantization (IQ) variants, with IQ versions often showing better performance than similarly-sized standard quantizations.
- Multiple quantization options ranging from i1-IQ1_S (3.1GB) to i1-Q6_K (10.2GB)
- Improved matrix (imatrix) quantization for enhanced performance
- Optimized variants for different memory and performance requirements
- Q4_K_M (7.6GB) recommended for balanced performance
Core Capabilities
- Efficient deployment with various size/quality tradeoffs
- Support for both standard and improved quantization methods
- Optimized performance in memory-constrained environments
- Compatible with standard GGUF loading systems
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options using imatrix technology, allowing users to choose the optimal balance between model size and performance for their specific use case.
Q: What are the recommended use cases?
The Q4_K_M variant (7.6GB) is recommended for general use, offering an optimal balance of speed and quality. For resource-constrained environments, the IQ3 variants provide good performance at smaller sizes, while Q6_K offers near-original quality for users with more resources available.
