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README.md

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 <b><font size="6">OpenGVLab</font></b>
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-Welcome to OpenGVLab! We are a research group from Shanghai AI Lab focused on Vision-Centric AI research. The GV in our name, OpenGVLab, means general vision, a general understanding of vision, so little effort is needed to adapt to new vision-based tasks.
+Welcome to OpenGVLab! Our admission is to co-evolve AI's safety and capability, marking the birth of the AI45 brand. With "Make Safe AI" as the core technology vision, the team focuses on strengthening the foundation of endogenous safety AI technology, promoting the industry's transformation from "Make AI Safe" to "Make Safe AI".This supports the healthy and orderly development of artificial intelligence in a beneficial, safe, and fair direction.
 
 # Models
 
-- [InternVL](https://github.com/OpenGVLab/InternVL): a pioneering open-source alternative to GPT-4V.
-- [InternImage](https://github.com/OpenGVLab/InternImage): a large-scale vision foundation models with deformable convolutions.
-- [InternVideo](https://github.com/OpenGVLab/InternVideo): large-scale video foundation models for multimodal understanding.
-- [VideoChat](https://github.com/OpenGVLab/Ask-Anything): an end-to-end chat assistant for video comprehension. 
-- [All-Seeing-Project](https://github.com/OpenGVLab/all-seeing): towards panoptic visual recognition and understanding of the open world.
+- [SafeWork-R1](https://github.com/AI45Lab/SafeWork-R1): a pioneering open-source safety-oriented reasoning model.
 
-# Datasets
-
-- [ShareGPT4o](https://sharegpt4o.github.io/): a groundbreaking large-scale resource that we plan to open-source with 200K meticulously annotated images, 10K videos with highly descriptive captions, and 10K audio files with detailed descriptions.
-- [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid): a large-scale video-text dataset for multimodal understanding and generation.
-- [MMPR](https://huggingface.co/datasets/OpenGVLab/MMPR): a high-quality, large-scale multimodal preference dataset.
-
-# Benchmarks
-- [MVBench](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2): a comprehensive benchmark for multimodal video understanding.
-- [CRPE](https://github.com/OpenGVLab/all-seeing/tree/main/all-seeing-v2): a benchmark covering all elements of the relation triplets (subject, predicate, object), providing a systematic platform for the evaluation of relation comprehension ability.
-- [MM-NIAH](https://github.com/uni-medical/GMAI-MMBench): a comprehensive benchmark for long multimodal documents comprehension.
-- [GMAI-MMBench](https://huggingface.co/datasets/OpenGVLab/GMAI-MMBench): a comprehensive multimodal evaluation benchmark towards general medical AI.