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: 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.
Source: http://arxiv.org/abs/2408.08872v1
