| Property | Value |
|---|---|
| Author | MiniMaxAI |
| Architecture | ViT-MLP-LLM |
| Vision Transformer Size | 303M parameters |
| Paper | arXiv:2501.08313 |
| Model Access | Hugging Face |
What is MiniMax-VL-01?
MiniMax-VL-01 is a cutting-edge multimodal vision-language model that combines a Vision Transformer with MiniMax-Text-01 through a two-layer MLP projector. The model represents a significant advancement in multimodal AI, capable of processing images with dynamic resolutions from 336×336 to 2016×2016 pixels.
Implementation Details
The model architecture consists of three key components: a 303-million-parameter Vision Transformer (ViT) for visual encoding, a two-layer MLP projector for image adaptation, and the MiniMax-Text-01 as the base language model. The training process involved 694 million image-caption pairs and processed 512 billion tokens across four distinct stages.
- Dynamic resolution processing with adaptive patch splitting
- Non-overlapping patch encoding with thumbnail preservation
- Comprehensive training on caption, description, and instruction data
- Quantization support for efficient deployment
Core Capabilities
- Strong performance on MMMU (68.5%) and MMMU-Pro (52.7%)
- Excellence in document understanding (DocVQA: 96.4%)
- Superior OCR capabilities (OCRBench: 865)
- Robust mathematical reasoning (MathVista: 68.6%)
- Effective long-context processing (M-LongDoc: 32.5%)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its dynamic resolution capability, allowing it to process images at various scales while maintaining a consistent thumbnail representation. This, combined with its comprehensive training on diverse data types, makes it particularly versatile for real-world applications.
Q: What are the recommended use cases?
The model excels in document analysis, visual question answering, mathematical reasoning with visual context, and general image understanding tasks. It's particularly well-suited for applications requiring both vision and language understanding, such as document processing, educational tools, and automated analysis systems.