SimpleScaling S1.1-32B GGUF
| Property | Value |
|---|---|
| Base Model | SimpleScaling S1.1-32B |
| Quantization Framework | LLAMA.cpp (b4671) |
| Model Size Range | 9.96GB - 65.54GB |
| Original Source | huggingface.co/simplescaling/s1.1-32B |
What is simplescaling_s1.1-32B-GGUF?
SimpleScaling S1.1-32B GGUF is a comprehensive collection of quantized versions of the original SimpleScaling 32B model, optimized for different deployment scenarios. The collection features various quantization levels from Q2 to Q8, including specialized formats for ARM and AVX systems, making it highly versatile for different hardware configurations and performance requirements.
Implementation Details
The model uses imatrix quantization techniques and offers multiple compression levels, each optimized for different use cases. The quantization variants range from the full F16 weights (65.54GB) to highly compressed IQ2_XS format (9.96GB), with various intermediate options balancing quality and size.
- Supports special Q8_0 embedding quantization for enhanced quality in specific variants
- Features online repacking capability for ARM and AVX CPU inference
- Implements new IQ (Integer Quantization) formats for improved efficiency
- Includes specialized variants for different hardware architectures
Core Capabilities
- Multiple quantization options supporting various hardware configurations
- Optimized performance on ARM and AVX systems through online repacking
- Enhanced quality options with Q8_0 embedding preservation
- Flexible deployment options from high-quality to highly compressed formats
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, from high-quality Q8_0 to highly compressed IQ2 variants, allowing users to choose the perfect balance between model size and performance for their specific use case. The implementation of advanced techniques like online repacking and specialized embedding quantization makes it particularly versatile across different hardware configurations.
Q: What are the recommended use cases?
For maximum quality, the Q6_K_L or Q5_K_M variants are recommended. For balanced performance, Q4_K_M is suggested as the default choice. For systems with limited RAM, the Q3_K_L or IQ3_M variants offer good performance while maintaining reasonable quality. The IQ2 variants are suitable for extremely resource-constrained environments where model size is the primary concern.
