Rombo-LLM-V3.1-QWQ-32b-GGUF
| Property | Value |
|---|---|
| Original Model | Rombo-LLM-V3.1-QWQ-32b |
| Quantization Framework | llama.cpp (b4792) |
| Size Range | 9.03GB - 34.82GB |
| Author | bartowski |
What is Rombo-Org_Rombo-LLM-V3.1-QWQ-32b-GGUF?
This is a comprehensive collection of quantized versions of the Rombo-LLM-V3.1 32B model, specifically optimized using llama.cpp's imatrix quantization technology. The collection offers 26 different quantization variants, ranging from extremely high quality (Q8_0) to highly compressed (IQ2_XXS), allowing users to balance performance and resource requirements.
Implementation Details
The model uses a specialized prompt format with system, user, and assistant markers. All quantizations were performed using the imatrix option, providing optimized performance across different compression levels. The implementation includes special handling for embed/output weights in certain variants (Q3_K_XL, Q4_K_L, etc.) using Q8_0 quantization for these specific weights.
- Supports multiple quantization methods including K-quants and I-quants
- Implements online repacking for ARM and AVX CPU inference
- Offers specialized variants for different hardware configurations
- Includes SOTA techniques for maintaining usability even in heavily compressed versions
Core Capabilities
- Flexible deployment options from 9GB to 35GB variants
- Optimized performance on both CPU and GPU configurations
- Support for various inference backends including cuBLAS, rocBLAS, and Apple Metal
- Special optimizations for ARM and AVX architectures
Frequently Asked Questions
Q: What makes this model unique?
The model offers an unprecedented range of quantization options with carefully optimized variants for different hardware configurations and use cases. It implements cutting-edge techniques like online repacking and specialized embed/output weight handling to maintain quality even at high compression rates.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q8_0 variants with sufficient RAM/VRAM. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained systems, IQ3_M or IQ2_M provide surprisingly usable performance at smaller sizes.
