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Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

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arxiv 2502.09927 v1 pith:6YGP4SWU submitted 2025-02-14 cs.CV cs.AI

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

Granite Vision Team: Leonid Karlinsky , Assaf Arbelle , Abraham Daniels , Ahmed Nassar , Amit Alfassi , Bo Wu , Eli Schwartz , Dhiraj Joshi
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Jovana Kondic Nimrod Shabtay Pengyuan Li Roei Herzig Shafiq Abedin Shaked Perek Sivan Harary Udi Barzelay Adi Raz Goldfarb Aude Oliva Ben Wieles Bishwaranjan Bhattacharjee Brandon Huang Christoph Auer Dan Gutfreund David Beymer David Wood Hilde Kuehne Jacob Hansen Joseph Shtok Ken Wong Luis Angel Bathen Mayank Mishra Maksym Lysak Michele Dolfi Mikhail Yurochkin Nikolaos Livathinos Nimrod Harel Ophir Azulai Oshri Naparstek Rafael Teixeira de Lima Rameswar Panda Sivan Doveh Shubham Gupta Subhro Das Syed Zawad Yusik Kim Zexue He Alexander Brooks Gabe Goodhart Anita Govindjee Derek Leist Ibrahim Ibrahim Aya Soffer David Cox Kate Soule Luis Lastras Nirmit Desai Shila Ofek-koifman Sriram Raghavan Tanveer Syeda-Mahmood Peter Staar Tal Drory Rogerio Feris
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classification cs.CV cs.AI
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We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extraction from tables, charts, diagrams, sketches, and infographics, as well as general image tasks. The architecture of Granite Vision is centered around visual modality alignment with a decoder-only, 2 billion parameter Granite large language model. Additionally, we introduce a dedicated safety classification approach in test-time that leverages a sparse set of attention vectors to identify potential harmful inputs. Despite its lightweight architecture, Granite Vision achieves strong results in standard benchmarks related to visual document understanding, as well as on the LiveXiv benchmark, which is designed to avoid test set contamination by using a constantly updated corpus of recently published Arxiv papers. We are releasing the model under the Apache-2 license, allowing for both research and commercial use, while offering complete visibility into the training data and other relevant details. See https://huggingface.co/ibm-granite/ for model weights.

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