Levlex-Math-One-14B-GGUF
| Property | Value |
|---|---|
| Original Model | numinousmuses/Levlex-Math-One-14B |
| Quantized By | mradermacher |
| Model Format | GGUF |
| Size Range | 5.7GB - 15.7GB |
| Repository | HuggingFace |
What is Levlex-Math-One-14B-GGUF?
Levlex-Math-One-14B-GGUF is a quantized version of the original Levlex-Math-One-14B model, optimized for efficient deployment while maintaining performance. This version offers multiple quantization options to balance between model size and quality, making it more accessible for various computing environments.
Implementation Details
The model provides several quantization types, each optimized for different use cases. The implementation includes static quantizations ranging from Q2_K (5.7GB) to Q8_0 (15.7GB), with various intermediate options offering different trade-offs between size and quality.
- Q4_K_S (8.5GB) and Q4_K_M (9.0GB) are recommended for fast performance
- Q6_K (12.1GB) offers very good quality
- Q8_0 (15.7GB) provides the best quality while maintaining speed
- IQ4_XS (8.2GB) represents an intelligent quantization option
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Optimized performance-to-size ratios
- Compatible with standard GGUF file usage
- Maintains mathematical capabilities of the original model
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its variety of quantization options, allowing users to choose the optimal balance between model size and performance for their specific use case. The availability of both standard and intelligent quantization (IQ) options provides flexibility in deployment.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they offer a good balance of speed and quality. For scenarios requiring maximum quality, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K_S variants.
