Mistral-Small-3.1-24B-Instruct GGUF
| Property | Value |
|---|---|
| Base Model | Mistral-Small-3.1-24B-Instruct-2503 |
| Quantization Range | 6.55GB - 47.15GB |
| Original Model URL | https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503 |
| Format | GGUF (llama.cpp compatible) |
What is mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF?
This is a comprehensive collection of GGUF quantized versions of Mistral's 24B parameter instruction-tuned language model. The repository provides multiple quantization options ranging from full BF16 precision (47.15GB) down to highly compressed IQ2_XXS (6.55GB), enabling deployment across various hardware configurations and performance requirements.
Implementation Details
The model uses llama.cpp's imatrix quantization with specialized calibration datasets. It implements a specific prompt format: <s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]. The quantization variants include novel techniques like embed/output weight preservation in Q8_0 for certain versions to maintain quality while reducing size.
- Multiple quantization options from BF16 to IQ2
- Support for online weight repacking for ARM and AVX CPU inference
- Specialized quantizations (Q3_K_XL, Q4_K_L) with Q8_0 embeddings
- Compatible with LM Studio and any llama.cpp based project
Core Capabilities
- Flexible deployment options across different hardware configurations
- Optimized performance for both CPU and GPU inference
- Quality-size tradeoffs suitable for various use cases
- Support for both high-end and resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptional range of quantization options with detailed performance characteristics, allowing users to choose the perfect balance between model size, quality, and hardware requirements. The implementation of advanced techniques like SOTA quantization and online repacking makes it highly versatile.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q5_K_L variants. For balanced performance, Q4_K_M is recommended as the default choice. For resource-constrained environments, IQ4_XS offers good quality at smaller sizes. GPU users should choose a quantization 1-2GB smaller than their available VRAM.
