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README.md

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-* **`2024.11.14`** 🌟 MME-RealWorld now has a [lite version](https://huggingface.co/datasets/yifanzhang114/MME-RealWorld-Lite) (50 samples per task) for inference acceleration, which is also supported by VLMEvalKit and Lmms-eval.
-* **`2024.10.27`** 🌟 LLaVA-OV currently ranks first on our leaderboard, but its overall accuracy remains below 55%, see our [leaderboard](https://mme-realworld.github.io/home_page.html#leaderboard) for the detail.
+* **`2024.11.14`** 🌟 MME-RealWorld now has a [lite version](https://huggingface.co/datasets/yifanzhang114/MME-RealWorld-Lite) (50 samples per task, or all if fewer than 50) for inference acceleration, which is also supported by VLMEvalKit and Lmms-eval.
 * **`2024.09.03`** 🌟 MME-RealWorld is now supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) and [Lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) repository, enabling one-click evaluation—give it a try!" 
 * **`2024.08.20`** 🌟 We are very proud to launch MME-RealWorld, which contains 13K high-quality images, annotated by 32 volunteers, resulting in 29K question-answer pairs that cover 43 subtasks across 5 real-world scenarios. As far as we know, **MME-RealWorld is the largest manually annotated benchmark to date, featuring the highest resolution and a targeted focus on real-world applications**.