qihoo360 TinyR1-32B Preview GGUF
| Property | Value |
|---|---|
| Base Model | TinyR1-32B |
| Original Source | huggingface.co/qihoo360/TinyR1-32B-Preview |
| Quantization Range | 9.96GB - 34.82GB |
| Author | bartowski |
What is qihoo360_TinyR1-32B-Preview-GGUF?
This is a comprehensive collection of GGUF quantizations of the TinyR1-32B model, optimized for different deployment scenarios. The quantizations range from extremely high quality (Q8_0) to highly compressed versions (IQ2_XS), enabling users to balance performance and resource requirements.
Implementation Details
The model uses llama.cpp's imatrix quantization technology, offering various quantization methods including K-quants and I-quants. Each variant is optimized for specific hardware configurations and use cases, with special attention to embed/output weight handling in certain versions.
- Multiple quantization options from Q8_0 (34.82GB) to IQ2_XS (9.96GB)
- Specialized versions with Q8_0 embed/output weights for enhanced performance
- Support for online repacking on ARM and AVX systems
- Optimized prompting format with system and user markers
Core Capabilities
- Flexible deployment options across different hardware configurations
- Optimized performance on both CPU and GPU implementations
- Support for various inference engines including LM Studio and llama.cpp
- Advanced weight handling for ARM and AVX architectures
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, making it adaptable to various hardware constraints while maintaining performance. It implements both traditional K-quants and newer I-quants, providing cutting-edge compression techniques.
Q: What are the recommended use cases?
For maximum quality, use Q8_0 or Q6_K_L variants if you have sufficient RAM. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained systems, I-quants like IQ4_XS offer good performance with smaller sizes.
