FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-GGUF
| Property | Value |
|---|---|
| Base Model Size | 32B parameters |
| Original Model | FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview |
| Quantization Method | GGUF with imatrix |
| Size Range | 9.03GB - 65.54GB |
What is FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-GGUF?
This is a comprehensive collection of quantized versions of the FuseO1-DeepSeekR1 32B model, optimized for different hardware configurations and use cases. The quantizations were created using llama.cpp release b4514 with imatrix options, offering various compression levels while maintaining different quality-performance tradeoffs.
Implementation Details
The model provides multiple quantization options ranging from full BF16 weights (65.54GB) down to highly compressed IQ2_XXS (9.03GB). Each quantization level offers different benefits:
- Q8_0: Extremely high quality quantization (34.82GB)
- Q6_K series: Near-perfect quality with significant size reduction (26-27GB)
- Q5_K series: High quality recommended variants (22-23GB)
- Q4_K series: Good quality standard options (18-20GB)
- Q3_K and IQ3 series: Lower quality but usable (13-17GB)
- IQ2 series: Surprisingly usable despite high compression (9-11GB)
Core Capabilities
- Supports multiple hardware configurations including ARM and AVX systems
- Online weight repacking for optimized performance
- Special quantizations with Q8_0 embed/output weights for enhanced quality
- Compatible with LM Studio and other inference engines
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, allowing users to find the perfect balance between model size, quality, and performance for their specific hardware setup. It includes both traditional K-quants and newer I-quants, with special optimizations for embed/output weights.
Q: What are the recommended use cases?
For maximum quality with sufficient RAM, use Q6_K_L or Q5_K_L variants. For balanced performance, Q4_K_M is recommended as the default choice. For systems with limited RAM, the IQ3 and IQ2 series provide surprisingly usable results despite high compression.
