Mistral-Small-3.1-24B-Base-2503
| Property | Value |
|---|---|
| Parameter Count | 24 Billion |
| Context Window | 128,000 tokens |
| License | Apache 2.0 |
| Model URL | https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503 |
| Tokenizer | Tekken (131k vocabulary) |
What is Mistral-Small-3.1-24B-Base-2503?
Mistral-Small-3.1-24B-Base-2503 is an advanced multimodal language model that builds upon Mistral Small 3, introducing state-of-the-art vision understanding capabilities while maintaining exceptional text processing performance. This 24B parameter model represents a significant advancement in AI technology, combining robust language understanding with visual processing capabilities.
Implementation Details
The model utilizes a Tekken tokenizer with a 131k vocabulary size and supports an impressive 128k context window. It's designed as a base model, serving as the foundation for the instruction-tuned version Mistral-Small-3.1-24B-Instruct-2503. Implementation is recommended through the vLLM library, which requires specific setup procedures for optimal performance.
- Advanced vision processing capabilities for image analysis
- Extensive multilingual support for 24+ languages
- Apache 2.0 licensed for commercial and non-commercial use
- Optimized for large context processing up to 128k tokens
Core Capabilities
- Strong performance on key benchmarks (MMLU: 81.01%, TriviaQA: 80.50%)
- Comprehensive multilingual support including major Asian and European languages
- Advanced visual content analysis and understanding
- Seamless integration of text and vision modalities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of large-scale language modeling (24B parameters) with advanced vision capabilities, while maintaining state-of-the-art performance across both modalities. The extensive context window of 128k tokens and broad multilingual support make it particularly versatile.
Q: What are the recommended use cases?
The model is ideal for applications requiring sophisticated text and image understanding, including multimodal analysis, cross-lingual applications, and tasks requiring extended context comprehension. However, as a base model, it requires instruction tuning for production deployment.
