OpenThinker2-32B-GGUF
| Property | Value |
|---|---|
| Original Model | OpenThinker2-32B |
| Quantization Types | Multiple (Q2-Q8) |
| Author | bartowski |
| Format | GGUF with imatrix calibration |
What is open-thoughts_OpenThinker2-32B-GGUF?
OpenThinker2-32B-GGUF is a comprehensive collection of quantized versions of the OpenThinker2 32B model, optimized for various hardware configurations and use cases. The model uses llama.cpp's advanced quantization techniques with imatrix calibration to provide multiple compression levels while maintaining performance.
Implementation Details
The model offers various quantization levels from Q2 to Q8, with file sizes ranging from 9GB to 65GB. Each quantization type is optimized for specific use cases, with newer formats like IQ4 and IQ3 incorporating state-of-the-art techniques for better performance-to-size ratios.
- Utilizes llama.cpp release b5035 for quantization
- Implements imatrix option with specialized dataset
- Supports online repacking for ARM and AVX CPU inference
- Special versions with Q8_0 quantization for embeddings and output weights
Core Capabilities
- Multiple quantization options for different hardware constraints
- Optimized versions for both CPU and GPU deployment
- Support for various deployment environments including LM Studio
- Special prompt format with system, user, and assistant messages
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options with carefully optimized performance characteristics, allowing users to choose the perfect balance between model size, quality, and hardware requirements. The implementation of imatrix calibration and special handling of embedding/output weights makes it particularly efficient.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (19.85GB) is recommended as the default choice, offering a good balance of quality and size. For high-end systems, Q6_K_L (27.26GB) provides near-perfect quality, while systems with limited RAM can use Q3_K_L (17.25GB) or lower quantizations.
