huihui-ai QwQ-32B Abliterated GGUF
| Property | Value |
|---|---|
| Original Model | QwQ-32B-abliterated |
| Author | bartowski |
| Size Range | 9GB - 35GB |
| Format | GGUF (llama.cpp compatible) |
What is huihui-ai_QwQ-32B-abliterated-GGUF?
This is a comprehensive collection of quantized versions of the QwQ-32B-abliterated model, optimized for different use cases and hardware configurations. The model offers various quantization levels from Q8_0 (highest quality) to IQ2_XXS (smallest size), allowing users to balance between performance and resource requirements.
Implementation Details
The model uses llama.cpp's imatrix quantization technology and comes in multiple variants optimized for different scenarios. Each variant offers specific trade-offs between model size, quality, and performance.
- Quantization ranges from Q8_0 (34.82GB) to IQ2_XXS (9.03GB)
- Special variants with Q8_0 embed/output weights for enhanced performance
- Support for online repacking for ARM and AVX CPU inference
- Compatible with LM Studio and other llama.cpp-based projects
Core Capabilities
- Multiple quantization options for different hardware configurations
- Optimized variants for ARM and AVX architectures
- Support for both high-quality inference (Q8_0, Q6_K) and resource-constrained environments (IQ2 series)
- Specialized versions with enhanced embed/output weight handling
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, from extremely high quality (Q8_0) to very compressed versions (IQ2_XXS), making it adaptable to almost any hardware setup while maintaining usable performance.
Q: What are the recommended use cases?
For users with ample RAM, the Q6_K_L variant is recommended for near-perfect quality. For balanced performance, Q4_K_M is the default choice. For resource-constrained systems, the IQ3/IQ2 series offers surprisingly usable performance at smaller sizes.
