Mistral-Small-3.1-24B-Instruct-2503
| Property | Value |
|---|---|
| Parameter Count | 24 Billion |
| Context Window | 128,000 tokens |
| License | Apache 2.0 |
| Tokenizer | Tekken (131k vocabulary) |
| Model URL | https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503 |
What is Mistral-Small-3.1-24B-Instruct-2503?
Mistral-Small-3.1-24B-Instruct-2503 represents a significant advancement in multimodal AI models, combining state-of-the-art vision understanding with enhanced long-context capabilities. This instruction-tuned model builds upon its base version, packing 24 billion parameters while maintaining exceptional efficiency - capable of running on a single RTX 4090 or 32GB RAM MacBook when quantized.
Implementation Details
The model employs a Tekken tokenizer with a 131k vocabulary size and supports an impressive 128k context window. It's designed for deployment through vLLM, with recommended temperature settings of 0.15 for optimal performance. The model includes robust support for system prompts and maintains consistent performance across multiple modalities.
- Advanced vision capabilities for image analysis and understanding
- Multilingual support for dozens of languages including European, East Asian, and Middle Eastern languages
- Native function calling and JSON output capabilities
- State-of-the-art conversational and reasoning abilities
Core Capabilities
- Vision understanding with high performance on benchmarks like MMMU (64.00%) and DocVQA (94.08%)
- Multilingual processing with strong performance across different language families (71.18% average)
- Long-context handling with impressive results on RULER 128K (81.20%)
- Programming and mathematical reasoning with strong scores on HumanEval (88.41%)
- Fast-response conversational abilities ideal for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance of capabilities across text, vision, and multilingual tasks while maintaining deployability on consumer hardware. It's particularly notable for achieving top-tier performance in vision tasks while preserving strong text processing abilities.
Q: What are the recommended use cases?
The model excels in fast-response conversational applications, low-latency function calling, subject matter expertise via fine-tuning, local inference for sensitive data handling, programming tasks, mathematical reasoning, and comprehensive document understanding with visual components.
