IBM AI Releases Granite – Vision 3.1-2b: A small visual language model with super impressive performance on a variety of tasks

The integration of visual and textual data in artificial intelligence presents a complex challenge. Traditional models often have difficulty interpreting structured visual documents such as tables, charts, charts, and charts. This limitation affects automated content extraction and understanding, which is critical for application in data analysis, information retrieval and decision-making. As organizations increasingly rely on AI-driven insights, the need for models that can effectively process visual and textual information has grown significantly.

IBM solves this challenge with the release of Granite-Vision-3.1-2ba compact visual model for document understanding. The model is able to extract content from a variety of visual formats, including tables, charts, and charts. Training on well-curated datasets including public and synthetic sources is designed to handle a wide range of document-related tasks. Granite-Vision-3.1-2b is fine-tuned through a large granite language model, integrating image and text methods to improve its interpretability and make it suitable for various practical applications.

The model consists of three key components:

1. Visual Encoder: Use siglip to efficiently process and encode visual data.
2. Visual connector: Two-layer multi-layer perceptron (MLP) with GELU activation capability designed to bridge visual and text information.
3. Big language model: Built on Granite-3.1-2B-teaching, with 128K context length for handling complex and extensive inputs.

The training process is based on Llava and combines multi-layer encoder functionality, as well as density resolution resolution in Anyres. These enhancements improve the model’s ability to understand detailed visual content. The architecture allows the model to perform a variety of visual document tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering document-based queries with higher accuracy.

The evaluation showed that granite Vision-3.1-2b performed well in multiple benchmarks, especially in terms of document understanding. For example, it scored 0.86 on the ChartQA benchmark, surpassing other models in the 1B-4B parameter range. In the TextVQA benchmark, it scored 0.76, showing strong performance when interpreting and answering questions based on text information embedded in the image. These results highlight the model’s potential in enterprise applications that require precise visual and text data processing.

IBM’s Granite-Vision-3.1-2b represents a significant advance in visual models, providing a balanced approach to visual document understanding. Its architecture and training approach enables it to effectively interpret and analyze complex visual and text data. With native support for transformers and VLLM, this model is suitable for a variety of use cases and can be deployed in cloud-based environments such as Colab T4. This accessibility makes it a practical tool for researchers and professionals who want to enhance AI-driven document processing capabilities.

Check IBM Granite/Granite-Vision-3.1-2b-preiview and IBM Granite/Granite-3.1-2B-Instruct. All credits for this study are to the researchers on the project. Also, don’t forget to follow us twitter And join us Telegram Channel and LinkedIn GrOUP. Don’t forget to join us 75K+ ml reddit.

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Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically and be understood by a wide audience through technical voices and also by a wide audience. . The platform has over 2 million views per month, demonstrating its popularity among its audience.