olmner-sbr-7b-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Type | GGUF Quantized |
| Original Source | marcuscedricridia/olmner-sbr-7b |
| Size Range | 3.1GB - 15.3GB |
What is olmner-sbr-7b-GGUF?
olmner-sbr-7b-GGUF is a quantized version of the olmner-sbr-7b model, offering various compression levels to optimize for different usage scenarios. This implementation provides multiple GGUF quantization variants, making the model more accessible for deployment across different hardware configurations.
Implementation Details
The model offers 12 different quantization variants, each optimized for specific use cases. The quantization types range from highly compressed Q2_K (3.1GB) to full precision f16 (15.3GB), with recommended variants being Q4_K_S and Q4_K_M that provide an optimal balance between speed and quality.
- Q4_K variants (4.6-4.8GB) - Recommended for general use, offering fast performance
- Q6_K variant (6.4GB) - Provides very good quality with moderate compression
- Q8_0 variant (8.2GB) - Offers the best quality while maintaining speed
- IQ4_XS variant (4.3GB) - Special quantization type for specific use cases
Core Capabilities
- Multiple quantization options for different hardware requirements
- Optimized variants for speed vs quality tradeoffs
- Compatible with standard GGUF loading mechanisms
- Supports various deployment scenarios from resource-constrained to high-performance environments
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the perfect balance between model size, inference speed, and quality for their specific use case.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they provide a good balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K variants.
