H2O.ai’s Mississippi models exemplify this new approach. The recently released Mississippi-2B, with just 2.1 billion parameters, and its even smaller sibling Mississippi-0.8B, are revolutionizing document processing and OCR tasks. What’s remarkable isn’t just their size, but their performance. The 0.8B version consistently outperforms models 20 times its size on OCRBench.
The secret lies in their architecture. Instead of trying to be generalists, these models employ specialized techniques like 448×448 pixel tiling for image processing, allowing them to maintain high accuracy while keeping computational requirements modest. They’re trained on carefully curated datasets – 17.2 million examples for the 2B version and 19 million for the 0.8B model – focusing on quality over quantity.