QwQ-32B-Snowdrop GGUF Quantizations
| Property | Value |
|---|---|
| Original Model | QwQ-32B-Snowdrop-v0 |
| Quantization Types | 27 variants (BF16 to IQ2_XXS) |
| Size Range | 9.03GB - 65.53GB |
| Author | bartowski |
| Model Link | Original Model |
What is trashpanda-org_QwQ-32B-Snowdrop-v0-GGUF?
This is a comprehensive suite of GGUF quantizations for the QwQ-32B-Snowdrop model, offering 27 different variants optimized for various use cases and hardware configurations. The quantizations range from full BF16 precision to highly compressed IQ2 formats, allowing users to balance model quality with hardware constraints.
Implementation Details
The quantizations were created using llama.cpp release b4867 with imatrix options. The suite includes both traditional K-quants and newer I-quants, with special optimizations for embed/output weights in certain versions.
- Highest quality options: BF16 (65.53GB) and Q8_0 (34.82GB)
- Recommended balanced options: Q6_K_L (27.26GB) and Q5_K_M (23.26GB)
- Memory-efficient options: IQ3_XXS (12.84GB) and IQ2_XXS (9.03GB)
Core Capabilities
- Online weight repacking for ARM and AVX CPU inference
- Special Q8_0 embed/output weights in _L variants
- SOTA compression techniques in I-quant variants
- Compatible with LM Studio and llama.cpp-based projects
Frequently Asked Questions
Q: What makes this model unique?
This quantization suite offers an unprecedented range of compression options for a 32B parameter model, with careful optimizations for different hardware architectures and novel techniques like I-quants for improved efficiency.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q5_K_M variants. For balanced performance, Q4_K_M is recommended. For systems with limited RAM, the IQ3/IQ2 variants offer surprisingly usable performance at minimal size.
