Mistral-Small-24B-Instruct-2501-Q8_0-GGUF
| Property | Value |
|---|---|
| Model Size | 24B parameters |
| Format | GGUF (Q8 quantization) |
| Original Source | mistralai/Mistral-Small-24B-Instruct-2501 |
| Hugging Face Repository | Karsh-CAI/Mistral-Small-24B-Instruct-2501-Q8_0-GGUF |
What is Mistral-Small-24B-Instruct-2501-Q8_0-GGUF?
This is a converted version of the Mistral-Small-24B-Instruct model, optimized for deployment using llama.cpp. The model has been quantized to 8-bit precision (Q8) and converted to the GGUF format, making it more efficient for local inference while maintaining good performance.
Implementation Details
The model utilizes the GGUF format, which is specifically designed for efficient inference with llama.cpp. It can be deployed using either the CLI interface or as a server, supporting context windows up to 2048 tokens.
- Q8 quantization for balanced performance and efficiency
- Compatible with llama.cpp's latest features
- Supports both CLI and server deployment modes
- Easy integration with existing llama.cpp workflows
Core Capabilities
- Local inference support through llama.cpp
- Efficient memory usage through Q8 quantization
- Flexible deployment options (CLI or server)
- Support for various hardware configurations including CPU and GPU acceleration
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for local deployment through llama.cpp, combining the power of the original Mistral-Small-24B-Instruct model with efficient quantization and the GGUF format for improved accessibility and performance.
Q: What are the recommended use cases?
The model is ideal for users who need to run large language models locally with reasonable performance and resource requirements. It's particularly suitable for developers and researchers who prefer using llama.cpp for deployment and require a balance between model quality and resource efficiency.
