MODEL PROFILE

h2ovl-mississippi-2b

h2oai · h2ovl-mississippi-2b

The H2OVL-Mississippi-2B is a high-performing, general-purpose vision-language model developed by H2O.ai to handle a wide range of multimodal tasks. This model, with 2 billion parameters, excels in tasks such as image captioning, visual question answering (VQA), and document understanding, while maintaining efficiency for real-world applications. The Mississippi-2B model builds on the strong foundations of our H2O-Danube language models, now extended to integrate vision and language tasks. It competes with larger models across various benchmarks, offering a versatile and scalable solution for document AI, OCR, and multimodal reasoning. - 2 Billion Parameters: Balance between performance and efficiency, making it suitable for document processing, OCR, VQA, and more. - Optimized for Vision-Language Tasks: Achieves high performance across a wide range of applications, including document AI, OCR, and multimodal reasoning. - Comprehensive Dataset: Trained on 17M image-text pairs, ensuring broad coverage and strong task generalization.

Text generationmultimodal large language modellicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/1 01:04:42
OVERVIEW

Model overview

The H2OVL-Mississippi-2B is a high-performing, general-purpose vision-language model developed by H2O.ai to handle a wide range of multimodal tasks. This model, with 2 billion parameters, excels in tasks such as image captioning, visual question answering (VQA), and document understanding, while maintaining efficiency for real-world applications. The Mississippi-2B model builds on the strong foundations of our H2O-Danube language models, now extended to integrate vision and language tasks. It competes with larger models across various benchmarks, offering a versatile and scalable solution for document AI, OCR, and multimodal reasoning. - 2 Billion Parameters: Balance between performance and efficiency, making it suitable for document processing, OCR, VQA, and more. - Optimized for Vision-Language Tasks: Achieves high performance across a wide range of applications, including document AI, OCR, and multimodal reasoning. - Comprehensive Dataset: Trained on 17M image-text pairs, ensuring broad coverage and strong task generalization.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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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。