xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

• URL: http://arxiv.org/abs/2408.08872v2
• Date: Wed, 28 Aug 2024 05:03:34 GMT
• Title: xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
• Authors: Le Xue, Manli Shu, Anas Awadalla, Jun Wang, An Yan, Senthil Purushwalkam, Honglu Zhou, Viraj Prabhu, Yutong Dai, Michael S Ryoo, Shrikant Kendre, Jieyu Zhang, Can Qin, Shu Zhang, Chia-Chih Chen, Ning Yu, Juntao Tan, Tulika Manoj Awalgaonkar, Shelby Heinecke, Huan Wang, Yejin Choi, Ludwig Schmidt, Zeyuan Chen, Silvio Savarese, Juan Carlos Niebles, Caiming Xiong, Ran Xu,
• Abstract summary: This report introduces xGen-MM, a framework for developing Large Multimodal Models (LMMs) The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks.
• Score: 157.44696790158784
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This report introduces xGen-MM (also known as BLIP-3), a framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. xGen-MM, short for xGen-MultiModal, expands the Salesforce xGen initiative on foundation AI models. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks. Our pre-trained base model exhibits strong in-context learning capabilities and the instruction-tuned model demonstrates competitive performance among open-source LMMs with similar model sizes. In addition, we introduce a safety-tuned model with DPO, aiming to mitigate harmful behaviors such as hallucinations and improve safety. We open-source our models, curated large-scale datasets, and our fine-tuning codebase to facilitate further advancements in LMM research. Associated resources will be available on our project page above.

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