With the widespread application of the Transformer architecture in various modalities,
including vision, the technology of large language models is evolving from a single modality …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

People are spending an enormous amount of time on digital devices through graphical user
interfaces (GUIs) eg computer or smartphone screens. Large language models (LLMs) such …

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …

Abstract The Large Vision-Language Model (LVLM) field has seen significant
advancements, yet its progression has been hindered by challenges in comprehending fine …

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-
world vision and language understanding applications. Our approach is structured around …

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding
the visual world. Conventional LMMs process images in fixed sizes and limited resolutions …

Multi-modal large language models (MLLMs) have been shown to efficiently integrate
natural language with visual information to handle multi-modal tasks. However MLLMs still …

We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that
supports long-contextual input and output. IXC-2.5 excels in various text-image …