We propose InternLM-XComposer, a vision-language large model that enables
advanced image-text comprehension and composition. The innovative nature of our
model is highlighted by three appealing properties: 1) Interleaved Text-Image
Composition: InternLM-XComposer can effortlessly generate coherent and
contextual articles that seamlessly integrate images, providing a more engaging
and immersive reading experience. Simply provide a title, and our system will
generate the corresponding manuscript. It can intelligently identify the areas
in the text where images would enhance the content and automatically insert the
most appropriate visual candidates. 2) Comprehension with Rich Multilingual
Knowledge: The text-image comprehension is empowered by training on extensive
multi-modal multilingual concepts with carefully crafted strategies, resulting
in a deep understanding of visual content. 3) State-of-the-art Performance: Our
model consistently achieves state-of-the-art results across various mainstream
benchmarks for vision-language foundational models, including MME Benchmark,
MMBench, MMBench-CN, Seed-Bench, and CCBench (Chinese Cultural Benchmark).
Collectively, InternLM-XComposer seamlessly blends advanced text-image
comprehension and composition, revolutionizing vision-language interaction and
offering new insights and opportunities. The InternLM-XComposer models with 7B
parameters are publicly available at
this https URL

我们提出了 InternLM-XComposer，一个能够实现高级图像 - 文本理解和组合的视觉语言模型。该模型具有三个吸引人的特点：1）交错式文本 - 图像组合：InternLM-XComposer 可以轻松生成连贯且情境感强的文章，将图像无缝地融入其中，提供更具吸引力和沉浸式的阅读体验。只需提供标题，我们的系统将生成相应的手稿。它能够智能地识别文本中可以增强内容的图像区域，并自动插入最合适的视觉候选。2）基于丰富的多语言知识的理解：通过在广泛的多模态多语言概念上进行训练，并采用精心设计的策略，增强了图片 - 文本的理解能力。3）最先进的性能：我们的模型在包括 MME 基准、MMBench、MMBench-CN、Seed-Bench 和 CCBench（中国文化基准）等各种主流视觉 - 语言基础模型基准测试中始终取得最先进的结果。综上所述，InternLM-XComposer 巧妙地融合了先进的文本 - 图像理解和组合，革新了视觉 - 语言交互，并提供了新的见解和机会。InternLM-XComposer 带有 70 亿参数的模型已公开提供于此 URL。