The integration of visual and textual data into artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, charts, infographics and diagrams with precision. This limitation affects automated content extraction and understanding, which is crucial to applications in data analysis, obtaining information and decision making. As organizations are increasingly dependent on AI-driven insight, the need for models that is able to effectively process both visual and textual information has grown significantly.
IBM has dealt with this challenge with the release of granite-vision-3.1-2BA compact vision -language model designed for document understanding. This model is capable of extracting content from different visual formats, including tables, charts and charts. Educated on a well -curated data set that includes both public and synthetic sources, it is designed to handle a wide range of document -related tasks. Fine tuned from a granite large language model, Granite-Vision-3.1-2B integrates image and text modalities to improve its interpretive capabilities, making it suitable for various practical uses.
The model consists of three key components:
- Vision Encoder: Uses Siglip to process and code visual data effectively.
- Vision-Language Connector: A two-layer multilayer perceptron (MLP) with gelu activation features designed to bridge visual and textual information.
- Large language model: Built on granite-3.1-2B instructions with a 128K context length for handling complex and extensive input.
The educational process is based on LLAVA and contains multilayer coding functions along with a closer grid solution in Anyredes. These improvements improve the model’s ability to understand detailed visual content. This architecture allows the model to perform various visual document tasks, such as analysis of tables and diagrams, perform optical character recognition (OCR) and answer document -based queries with greater accuracy.
Evaluations indicate that granite-vision-3.1-2B works well across multiple benchmarks, especially in document understanding. For example, it achieved a score of 0.86 on the Chartqa Benchmark, which surpassed other models within the 1B-4B parameter area. At the Textvqa Benchmarket, it achieved a score of 0.76, demonstrating strong performance in interpretation and answers to questions based on text information embedded in images. These results highlight the model’s potential for business applications that require precise visual and textual data processing.
IBM’s Granite-Vision-3.1-2B represents a remarkable progress in vision-language models that offers a well-balanced approach to visual document understanding. Its architecture and training methodology allows it to effectively interpret and analyze complex visual and textual data. With native support of transformers and VLLM, the model is adaptable to various use cases and can be implemented in cloud -based environments such as Colab T4. This availability makes it a practical tool for researchers and professionals who want to improve AI-driven document processing features.
Check out IBM Granite/Granite-Vision-3.1-2B Priction and IBM Granite/Granite-3.1-2B instruction. All credit for this research goes to the researchers in this project. Nor do not forget to follow us on Twitter and join in our Telegram Channel and LinkedIn GrOUP. Don’t forget to take part in our 75k+ ml subbreddit.
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