IBM AI Liberes GRANITE-VISION-3.1-2B: a small vision language model with super impressive performance in several tasks

The integration of visual and textual data in artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, pictures, infographics and diagrams with precision. This limitation affects the extraction and understanding of automated content, which are crucial for applications in data analysis, information recovery and decision making. As organizations increasingly rely on ideas promoted by ai, the need for models capable of effectively processing visual and textual information has grown significantly.

IBM has addressed this challenge with the launch of GRANITO-VISION-3.1-2BA compact vision language model designed for the understanding of documents. This model is able to extract content from various visual formats, including tables, pictures and diagrams. Trained in a well -cured data set that includes public and synthetic sources, it is designed to handle a wide range of documents related to the documents. Adjusted a large granite language model, Granite-Vision-3.1-2B integrates image and text modalities to improve its interpretive abilities, which makes it appropriate for various practical applications.

The model consists of three key components:

1. Vision encoder: Siglip uses to process and encode visual data efficiently.
2. Vision language connector: A two -layer multilayer perceptron (MLP) with Gelu activation functions, designed to close visual and textual information.
3. Large Language Model: Built on the Granite-3.1-2B instruction, with a context length of 128K to handle complex and extensive entries.

The training process is based on Llava and incorporates characteristics of multiple layer encoders, together with a dense grid resolution in Anyres. These improvements improve the capacity of the model to understand the detailed visual content. This architecture allows the model to perform several tasks of visual documents, such as analyzing tables and graphs, executing the recognition of optical characters (OCR) and responding consultations based on documents with greater precision.

The evaluations indicate that the vision of granite-3.1-2B works well at multiple reference points, particularly in the understanding of the documents. For example, he achieved a score of 0.86 at the point of reference of Chartqa, exceeding other models within the 1B-4B parameter range. At the Textvqa reference point, it reached a score of 0.76, demonstrating strong performance when interpreting and answering questions based on textual information integrated into images. These results highlight the potential of the model for business applications that require a precise processing of visual and textual data.

The Granite-3.1-2B vision of IBM represents a notable advance in the vision language models, which offers a well-balanced approach to understand visual documents. Your architecture and training methodology allows you to efficiently interpret and analyze complex visual and text data. With the native support for Transformers and VLLM, the model is adaptable to several use cases and can be implemented in cloud -based environments such as collaboration. This accessibility makes it a practical tool for researchers and professionals who seek to improve the processing capabilities of IA.

Verify he IBM-Granite/Granite-Vision-3.1-2B prior and IBM-Granite/Granite-3.1-2b-Instruct. All credit for this investigation goes to the researchers of this project. Besides, don't forget to follow us <a target="_blank" href="https://x.com/intent/follow?screen_name=marktechpost” target=”_blank” rel=”noreferrer noopener”>twitter and join our Telegram channel and LINKEDIN GRsplash. Do not forget to join our 75K+ ml of submen.

Recommended open source ai platform: 'Intellagent is a framework of multiple open source agents to evaluate the complex conversational system' _(Promoted)