H2O.AI UNVEILS COMPACT, COST EFFICIENT MULTIMODAL FOUNDATION MODELS FOR DOCUMENT AI

KUALA LUMPUR, Oct 18 (Bernama) -- H2O.ai, the leader in open-source generative artificial intelligence (GenAI), announced H2OVL Mississippi 2B and 0.8B, two powerful new multimodal foundation models designed specifically for optical character recognition (OCR) and Document AI use cases.

Compact yet highly efficient, the H2OVL Mississippi foundation models represent a significant advancement in AI, delivering unmatched performance for vision and OCR tasks in enterprise environments.

“We have designed H2OVL Mississippi models to be a high-performance yet cost-effective solution, bringing AI-powered OCR, visual understanding, and Document AI to businesses.

“By blending state-of-the-art multimodal AI with extreme efficiency, H2OVL Mississippi delivers precise, scalable Document AI solutions across a range of industries,” said H2O.ai chief executive officer and founder, Sri Ambati in a statement.

Available now on Hugging Face, H2OVL Mississippi 2B and 0.8B offer enterprises an economical solution with efficiency and accuracy for real-time document analysis and image recognition, making it easier to integrate powerful, lightweight AI into workflows that require top-tier OCR and document understanding.

Key features of H2OVL Mississippi 2B and 0.8B are lightweight model, multimodal mastery, tailored training, and real-time efficiency.

H2OVL Mississippi 2B builds on the legacy of H2O Danube2 with a robust 2.1 billion parameter model optimised for lightweight deployment and specialised multimodal architecture that blends language and computer vision to meet the growing demand for more economical multimodal OCR.

Meanwhile, built on the Danube3 0.5B, H2OVL Mississippi 0.8B model, pre-trained on 11 million conversation pairs and fine-tuned with an additional eight million, surpassed all comparable SLMs in the market on OCR benchmarks, delivering unmatched performance on text recognition.

-- BERNAMA