TinyR1-32B-Preview-GGUF
| Property | Value |
|---|---|
| Original Model | TinyR1-32B-Preview |
| Author | mradermacher |
| Model Format | GGUF |
| Source Repository | Hugging Face |
What is TinyR1-32B-Preview-GGUF?
TinyR1-32B-Preview-GGUF is a quantized version of the original TinyR1-32B model, optimized for efficient deployment and reduced storage requirements while maintaining performance. This implementation provides multiple quantization options to balance between model size and quality.
Implementation Details
The model offers various quantization levels, from highly compressed Q2_K (12.4GB) to high-quality Q8_0 (34.9GB). The implementation includes both standard and IQ (Improved Quantization) variants, with particular attention to optimization for different use cases.
- Q4_K_S (18.9GB) and Q4_K_M (20.0GB) variants are recommended for general use, offering good speed and quality balance
- Q6_K (27.0GB) provides very good quality with moderate compression
- Q8_0 (34.9GB) offers the best quality with minimal compression artifacts
- Special IQ4_XS variant available at 18.0GB for improved quality at lower sizes
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Size reduction from original model while maintaining functionality
- Optimized for various compute environments
- Compatible with standard GGUF loaders and frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the TinyR1-32B model, allowing users to choose the optimal balance between model size and quality for their specific use case. The availability of both standard and IQ quantization makes it particularly versatile.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they provide a good balance of speed and quality. For scenarios requiring highest quality, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the more compressed Q2_K or Q3_K variants.
