Mistral-Small-Sisyphus-24b GGUF Quantizations
| Property | Value |
|---|---|
| Original Model | allura-org/Mistral-Small-Sisyphus-24b-2503 |
| Quantization Method | llama.cpp imatrix |
| Size Range | 7.21GB - 25.05GB |
| Model Format | GGUF |
What is allura-org_Mistral-Small-Sisyphus-24b-2503-GGUF?
This is a comprehensive collection of quantized versions of the Mistral-Small-Sisyphus-24b model, optimized for different use cases and hardware configurations. The quantizations were created using llama.cpp's imatrix option, offering various compression levels while maintaining different quality-performance trade-offs.
Implementation Details
The model comes in multiple quantization formats, from high-precision Q8_0 (25.05GB) to highly compressed IQ2_XS (7.21GB). Each quantization level serves different needs, with some versions specifically optimized for embed/output weights using Q8_0 quantization.
- Uses specialized prompt format with system prompt support
- Implements both K-quants and I-quants for different hardware optimizations
- Supports online repacking for ARM and AVX CPU inference
- Offers various compression ratios with corresponding quality trade-offs
Core Capabilities
- High-quality inference with Q6_K_L and Q5_K_M variants
- Optimized performance on different hardware architectures (ARM, AVX)
- Memory-efficient options for limited RAM environments
- Compatible with LM Studio and other llama.cpp-based projects
Frequently Asked Questions
Q: What makes this model unique?
This model provides an extensive range of quantization options, allowing users to choose the perfect balance between model size, quality, and hardware compatibility. The implementation of both K-quants and I-quants, along with specialized optimizations for embed/output weights, makes it highly versatile.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q5_K_M variants. For balanced performance, Q4_K_M is recommended as the default choice. For systems with limited RAM, the IQ3 and IQ2 variants offer surprisingly usable performance at smaller sizes. The choice should be based on available VRAM/RAM and whether you're using CPU, NVIDIA, or AMD hardware.
