FluentlyLM-Prinum-abliterated-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Format | GGUF |
| Original Model | FluentlyLM-Prinum-abliterated |
| Available Quantizations | Q2_K to Q8_0 |
What is FluentlyLM-Prinum-abliterated-GGUF?
FluentlyLM-Prinum-abliterated-GGUF is a comprehensive collection of quantized versions of the original FluentlyLM-Prinum model, optimized for different use cases and hardware configurations. These quantizations provide various trade-offs between model size, inference speed, and output quality.
Implementation Details
The model offers multiple quantization options ranging from highly compressed (Q2_K at 12.4GB) to high-quality (Q8_0 at 34.9GB). The implementation includes both standard and IQ (Integer Quantization) variants, with particular attention to optimization for different deployment scenarios.
- Multiple quantization levels (Q2_K through Q8_0)
- Size options ranging from 12.4GB to 34.9GB
- Special IQ4_XS quantization at 18.0GB
- Recommended Q4_K variants for balanced performance
- Q6_K option for very good quality at 27.0GB
Core Capabilities
- Efficient model deployment with various size/quality trade-offs
- Fast inference with Q4_K variants
- High-quality text generation with Q6_K and Q8_0 variants
- Optimized for different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the FluentlyLM-Prinum model, allowing users to choose the optimal balance between model size, inference speed, and output quality for their specific use case.
Q: What are the recommended use cases?
For general use, the Q4_K_S and Q4_K_M variants are recommended due to their balance of speed and quality. For highest quality outputs, Q6_K or Q8_0 variants are recommended, while Q2_K and Q3_K variants are suitable for resource-constrained environments.
