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This was an absolute joy to read! Thank you for the excellent model/paper.
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Abstract
A comprehensive tutorial on building vision-language models, detailing the development of Idefics3-8B with an improved dataset enhancing document understanding.
The field of vision-language models (VLMs), which take images and texts as inputs and output texts, is rapidly evolving and has yet to reach consensus on several key aspects of the development pipeline, including data, architecture, and training methods. This paper can be seen as a tutorial for building a VLM. We begin by providing a comprehensive overview of the current state-of-the-art approaches, highlighting the strengths and weaknesses of each, addressing the major challenges in the field, and suggesting promising research directions for underexplored areas. We then walk through the practical steps to build Idefics3-8B, a powerful VLM that significantly outperforms its predecessor Idefics2-8B, while being trained efficiently, exclusively on open datasets, and using a straightforward pipeline. These steps include the creation of Docmatix, a dataset for improving document understanding capabilities, which is 240 times larger than previously available datasets. We release the model along with the datasets created for its training.
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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- A Single Transformer for Scalable Vision-Language Modeling (2024)
- OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding (2024)
- mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models (2024)
- EVLM: An Efficient Vision-Language Model for Visual Understanding (2024)
- SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models (2024)
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