Mistral-Small-3.1-24B-Instruct-2503-GGUF
| Property | Value |
|---|---|
| Parameter Count | 24B |
| Model Type | Instruction-tuned Language Model |
| Architecture | Mistral |
| Context Length | 128,000 tokens |
| Format | GGUF Quantized |
| Source | Hugging Face |
What is Mistral-Small-3.1-24B-Instruct-2503-GGUF?
This is a GGUF-quantized version of Mistral's 24B parameter instruction-tuned language model, optimized for efficient deployment while maintaining high performance. The model has been specifically quantized by bartowski using llama.cpp release b4914, making it more accessible for local deployment and integration.
Implementation Details
The model represents a significant advancement in large language model deployment, featuring GGUF quantization for optimal performance and resource utilization. It maintains the original model's capabilities while being optimized for practical applications.
- GGUF quantization based on llama.cpp release b4914
- 128k token context window
- Text-only version optimized for instruction-following
- Multi-language support across dozens of languages
Core Capabilities
- Advanced reasoning and agentic behavior
- Support for 24+ languages including English, French, German, Chinese, and Arabic
- Extended context handling up to 128k tokens
- Optimized for instruction-following tasks
- Efficient local deployment through GGUF quantization
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful 24B parameter architecture of Mistral with efficient GGUF quantization, making it particularly suitable for local deployment while maintaining strong performance across multiple languages and tasks. The extended 128k context window is notably larger than many comparable models.
Q: What are the recommended use cases?
The model is particularly well-suited for tasks requiring advanced reasoning, multi-language processing, and long-context understanding. It's optimized for instruction-following scenarios and can be effectively deployed in production environments where efficient resource utilization is crucial.
