We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model,
both in terms of size of the components and the breadth of its training task mixture.
Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context)  learning, as well as object detection, video question answering, and video captioning.
PaLI-X advances the state-of-the-art on most vision-and-language benchmarks considered (25+ of them) .
Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.