In an era defined by the explosive growth of data and rapid technological advancements,
Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence …

The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

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 …

We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally
reshaped the landscape of AI research and industry, shedding light on a promising path …

The remarkable progress of Multi-modal Large Language Models (MLLMs) has gained
unparalleled attention. However, their capabilities in visual math problem-solving remain …

The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of
information (the" needle") from long distractor texts (the" haystack"), has been widely …

Conversational large language models are fine-tuned for both instruction-following and
safety, resulting in models that obey benign requests but refuse harmful ones. While this …