Cran-May NQLSG-Qwen2.5-14B MegaFusion
| Property | Value |
|---|---|
| Base Model | Qwen2.5 14B |
| Quantization Types | Multiple (F16 to IQ2) |
| Original Size | 29.54GB (F16) |
| Minimum Size | 5.00GB (IQ2_S) |
| Author | Cran-May/bartowski |
What is Cran-May_NQLSG-Qwen2.5-14B-MegaFusion-v5-roleplay-duplicate-GGUF?
This is a comprehensive quantization suite of the Qwen2.5 14B model, specifically optimized for roleplay applications. The model offers various compression levels using llama.cpp's advanced quantization techniques, allowing users to choose the optimal balance between model size and performance for their specific hardware constraints.
Implementation Details
The model utilizes imatrix quantization with specialized calibration datasets, offering 24 different quantization variants ranging from full F16 precision to highly compressed IQ2 formats. Notable implementations include special handling of embedding and output weights in certain variants (like Q3_K_XL and Q4_K_L) using Q8_0 quantization for these specific layers.
- Implements specialized prompt format with system and user delimiters
- Offers online repacking for ARM CPU inference in specific variants
- Includes both K-quant and I-quant variants for different hardware optimizations
- Features special treatment of embedding/output weights in XL/L variants
Core Capabilities
- Roleplay-optimized responses and interactions
- Flexible deployment options from high-end to resource-constrained systems
- Hardware-specific optimizations for different architectures
- Memory efficiency while maintaining model quality
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, allowing deployment on various hardware configurations while maintaining optimal performance-to-size ratios. It's specifically tuned for roleplay applications and includes special optimizations for different CPU architectures.
Q: What are the recommended use cases?
For maximum quality, users should choose Q6_K_L or Q6_K variants. For balanced performance, Q4_K_M is recommended as the default choice. For systems with limited RAM, the IQ3/IQ2 variants offer surprisingly usable performance at minimal size requirements.
