qihoo360 TinyR1-32B Preview v0.2 GGUF
| Property | Value |
|---|---|
| Original Model | TinyR1-32B by qihoo360 |
| Quantization Framework | llama.cpp (b4792) |
| Model Hub | Hugging Face |
| Author | bartowski |
What is qihoo360_TinyR1-32B-Preview-v0.2-GGUF?
This is a comprehensive quantization suite of the TinyR1-32B model, offering multiple compression formats ranging from Q2 to Q8. The quantizations are specifically optimized using the imatrix option, providing various tradeoffs between model size, quality, and performance.
Implementation Details
The model uses a specific prompt format: <｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜><think>. It features different quantization levels suitable for various hardware configurations, from the high-quality Q8_0 (34.82GB) to the compact IQ2_XS (9.96GB).
- Advanced quantization techniques including embed/output weight optimization
- Online repacking support for ARM and AVX CPU inference
- Multiple compression options balancing quality and size
- SOTA techniques in lower quantizations maintaining usability
Core Capabilities
- Flexible deployment options from high-end to resource-constrained environments
- Optimized performance on different hardware architectures
- Special quantizations (Q3_K_XL, Q4_K_L) with Q8_0 embeddings for enhanced quality
- Automated weight repacking for improved ARM/AVX performance
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options with specific optimizations for different hardware architectures, making it highly versatile for various deployment scenarios. The implementation of both K-quants and I-quants provides users with options for different performance characteristics.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q8_0 variants if you have sufficient RAM. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained environments, the I-quants (IQ3_XXS, IQ2_XS) offer surprisingly usable performance at smaller sizes.
