| Property | Value |
|---|---|
| Original Model | NaniDAO/deepseek-r1-qwen-2.5-32B-ablated |
| Quantization Types | Multiple (BF16 to IQ2_XS) |
| File Size Range | 9.96GB - 65.54GB |
| Author | bartowski |
What is deepseek-r1-qwen-2.5-32B-ablated-GGUF?
This is a comprehensive collection of GGUF quantized versions of the Deepseek Qwen 32B model, optimized using llama.cpp's imatrix quantization technique. The collection provides various quantization levels from full BF16 precision down to highly compressed IQ2 formats, allowing users to balance model quality against hardware requirements.
Implementation Details
The model uses a specific prompt format: <｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>. The quantizations range from high-precision formats like Q8_0 (34.82GB) to highly compressed formats like IQ2_XS (9.96GB), each optimized for different use cases.
- Extensive quantization options (24 different variants)
- Special Q8_0 embedding weights for certain variants
- Online repacking support for ARM and AVX CPU inference
- Advanced IQ2/IQ3/IQ4 quantization techniques
Core Capabilities
- Flexible deployment options across different hardware configurations
- Optimized performance for various CPU architectures
- Memory-efficient options for resource-constrained environments
- Support for both high-quality and compressed inference
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options and optimization techniques, particularly the use of imatrix quantization and special handling of embedding weights. It offers solutions for virtually any hardware configuration while maintaining usable performance even in highly compressed formats.
Q: What are the recommended use cases?
For maximum quality, use Q8_0 or Q6_K_L variants. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained systems, the IQ3/IQ4 variants offer good performance-to-size ratios. The Q2 and IQ2 variants are suitable for extremely limited hardware while still maintaining surprisingly usable performance.