Mistral-Small-3.1-24B-Instruct-2503-HF-Q6_K-GGUF
| Property | Value |
|---|---|
| Original Author | anthracite-core |
| GGUF Conversion | WesPro |
| Model Size | 24B parameters |
| Format | GGUF with Q6_K quantization |
| Hugging Face Repository | Link |
What is Mistral-Small-3.1-24B-Instruct-2503-HF-Q6_K-GGUF?
This is a converted version of the Mistral-Small 24B parameter instruction-tuned language model, optimized for local deployment using llama.cpp. The model has been quantized using Q6_K precision, offering an excellent balance between model performance and resource efficiency.
Implementation Details
The model has been specifically converted to GGUF format, which is the latest format supported by llama.cpp for optimal performance. The Q6_K quantization scheme maintains high model quality while reducing memory requirements and improving inference speed.
- Converted from the original Hugging Face model to GGUF format
- Implements Q6_K quantization for efficient deployment
- Compatible with llama.cpp for local inference
- Supports both CLI and server deployment options
Core Capabilities
- Local deployment through llama.cpp
- Supports both command-line and server-based inference
- Compatible with standard llama.cpp deployment workflows
- Configurable context window up to 2048 tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for local deployment through the GGUF format and Q6_K quantization, making it possible to run a 24B parameter model efficiently on consumer hardware while maintaining good performance.
Q: What are the recommended use cases?
The model is ideal for users who need to run a powerful language model locally, particularly in scenarios where privacy, offline access, or custom deployment configurations are required. It's particularly well-suited for integration with llama.cpp-based applications.
