MODEL PROFILE

h2ovl-mississippi-800m

h2oai · h2ovl-mississippi-800m

The H2OVL-Mississippi-800M is a compact yet powerful vision-language model from H2O.ai, featuring 0.8 billion parameters. Despite its small size, it delivers state-of-the-art performance in text recognition, excelling in the Text Recognition segment of OCRBench and outperforming much larger models in this domain. Built upon the robust architecture of our H2O-Danube language models, the Mississippi-800M extends their capabilities by seamlessly integrating vision and language tasks. - 0.8 Billion Parameters: Balance between performance and efficiency, making it suitable for OCR and document processing. - Trained on 19 million image-text pairs, with a focus on OCR, document comprehension, and chart, figure, and table interpretation, the model is optimized for superior OCR performance. This guide demonstrates how to create prompts for extracting information and converting it into structured JSON outputs. It starts with basic examples and progresses to more complex JSON structures, including handling data from images of tables and charts. The objective is to help users design effective prompts that can be used in various applications, such as natural language processing, chatbots, or data extraction from visual inputs.

Text generationmultimodal large language modellicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 11:30:17
OVERVIEW

Model overview

The H2OVL-Mississippi-800M is a compact yet powerful vision-language model from H2O.ai, featuring 0.8 billion parameters. Despite its small size, it delivers state-of-the-art performance in text recognition, excelling in the Text Recognition segment of OCRBench and outperforming much larger models in this domain. Built upon the robust architecture of our H2O-Danube language models, the Mississippi-800M extends their capabilities by seamlessly integrating vision and language tasks. - 0.8 Billion Parameters: Balance between performance and efficiency, making it suitable for OCR and document processing. - Trained on 19 million image-text pairs, with a focus on OCR, document comprehension, and chart, figure, and table interpretation, the model is optimized for superior OCR performance. This guide demonstrates how to create prompts for extracting information and converting it into structured JSON outputs. It starts with basic examples and progresses to more complex JSON structures, including handling data from images of tables and charts. The objective is to help users design effective prompts that can be used in various applications, such as natural language processing, chatbots, or data extraction from visual inputs.

Key features

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。