  In the evolution of Vision-Language Pre-training, shifting from short-text
comprehension to encompassing extended textual contexts is pivotal. Recent
autoregressive vision-language models like \cite{flamingo, palme}, leveraging
the long-context capability of Large Language Models, have excelled in few-shot
text generation tasks but face challenges in alignment tasks. Addressing this
gap, we introduce the contrastive loss into text generation models, presenting
the COntrastive-Streamlined MultimOdal framework (\ModelName), strategically
partitioning the language model into dedicated unimodal text processing and
adept multimodal data handling components. \ModelName, our unified framework,
merges unimodal and multimodal elements, enhancing model performance for tasks
involving textual and visual data while notably reducing learnable parameters.
However, these models demand extensive long-text datasets, yet the availability
of high-quality long-text video datasets remains limited. To bridge this gap,
this work introduces \VideoDatasetName, an inaugural interleaved video-text
dataset featuring comprehensive captions, marking a significant step forward.
Demonstrating its impact, we illustrate how \VideoDatasetName{} enhances model
performance in image-text tasks. With 34% learnable parameters and utilizing
72\% of the available data, our model demonstrates significant superiority over
OpenFlamingo~\cite{openflamingo}. For instance, in the 4-shot flickr captioning
task, performance notably improves from 57.2% to 65.\%. The contributions of
\ModelName{} and \VideoDatasetName{} are underscored by notable performance
gains across 14 diverse downstream datasets encompassing both image-text and
video-text tasks.
