MFANN-Llama3.1-Abliterated-SLERP-V5-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | GGUF Quantized |
| Author | mradermacher |
| Base Model | netcat420/MFANN-Llama3.1-Abliterated-SLERP-V5 |
What is MFANN-Llama3.1-Abliterated-SLERP-V5-GGUF?
This is a quantized version of the MFANN-Llama3.1 model, specifically optimized for efficient deployment and reduced memory footprint while maintaining performance. The model offers multiple quantization variants ranging from 3.3GB to 16.2GB, allowing users to choose the optimal balance between model size and quality for their specific use case.
Implementation Details
The model implements various quantization techniques, including standard and improved quantization (IQ) methods. It provides options from Q2_K (3.3GB) to full F16 (16.2GB) precision, with recommended implementations being Q4_K_S and Q4_K_M for optimal performance-to-size ratio.
- Multiple quantization options available (Q2_K to F16)
- Specialized ARM optimization for certain variants
- IQ-quants available for enhanced quality at lower sizes
- Supports both static and weighted/imatrix quantization
Core Capabilities
- Efficient memory usage with various compression ratios
- Optimized for conversational AI applications
- English language support
- Compatible with standard GGUF implementations
- Flexible deployment options for different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its range of quantization options, allowing users to choose from multiple compression levels while maintaining quality. The availability of both standard and improved quantization methods provides flexibility for different use cases.
Q: What are the recommended use cases?
For most applications, the Q4_K_S (4.8GB) or Q4_K_M (5.0GB) variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, Q8_0 (8.6GB) is recommended, while Q2_K (3.3GB) is suitable for resource-constrained environments.
