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IBM has released Granite 3.2, a smaller AI model designed for efficient inference and practical applications. This release focuses on delivering strong performance with reduced computational resources.

With the continuous development of artificial intelligence technology, the integration of visual and textual data has become a complex challenge. Traditional models often struggle to accurately parse structured visual documents such as tables, charts, infographics, and diagrams. This limitation impacts automated content extraction and comprehension capabilities, subsequently affecting applications in data analysis, information retrieval, and decision-making. In response to this demand, IBM recently released Granite-Vision-3.1-2B, a compact visual language model specifically designed for document understanding.

Recently, IBM announced the launch of version 3.1 of its open source language model Granite, which brings many important enhancements. The new version of the model has been redesigned with a denser architecture, capable of handling up to 128,000 tokens at once. This update signifies a substantial improvement in Granite's ability to process complex text and tasks. The Granite 3.1 model has been trained on datasets in 12 languages and 116 programming languages, processing a total of 12 trillion tokens.

IBM has officially launched its next-generation open-source large language model, Granite3.1, aiming to secure a leading position in the enterprise AI sector. This series of models features an extended context length of 128K, embedding models, built-in hallucination detection capabilities, and significant performance improvements. IBM claims that the Granite8B Instruct model outperforms its open-source competitors of similar scale, including Meta's Llama3.1, Qwen2.5, and Google's Gemma2.

Red Hat recently released the latest version 1.3 of its enterprise-grade Linux AI platform, RHEL AI, which adds support for IBM's Granite large language model (LLM) and previews support for the Intel Gaudi 3 accelerator. This update brings more opportunities for Red Hat's service partners and system integrators to help enterprises explore and integrate AI applications more effectively. Joe Fernandez, Vice President and General Manager of Red Hat's AI business unit, stated that

IBM opened the Granite13B LLM model for enterprise applications in May. Now, Armand Ruiz, the vice president of IBM's AI platform products, has publicly disclosed the full content of the comprehensive 6.48TB dataset used to train Granite13B.The dataset, after strict preprocessing, was reduced to 2.07TB, a reduction of 68%. Ruiz emphasizes that this step is crucial for ensuring a high-quality, unbiased, ethical, and legally compliant dataset to meet the needs of enterprise applications. The datas