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

Xue, Le, Shu, Manli, Awadalla, Anas, Wang, Jun, Yan, An, Purushwalkam, Senthil, Zhou, Honglu, Prabhu, Viraj, Dai, Yutong, Ryoo, Michael S, Kendre, Shrikant, Zhang, Jieyu, Qin, Can, Zhang, Shu, Chen, Chia-Chih, Yu, Ning, Tan, Juntao, Awalgaonkar, Tulika Manoj, Heinecke, Shelby, Wang, Huan, Choi, Yejin, Schmidt, Ludwig, Chen, Zeyuan, Savarese, Silvio, Niebles, Juan Carlos, Xiong, Caiming, Xu, Ran

arXiv.org Artificial Intelligence 

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.