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README.md
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@@ -23,6 +23,14 @@ We introduce **MultiUI**, a dataset containing 7.3 million samples from 1 millio
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/vk7yT4Y7ydBOHM6BojmlI.mp4"></video>
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## Model Performance
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/h1L7J4rLlq6EOtbiXZjZW.png)
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/vk7yT4Y7ydBOHM6BojmlI.mp4"></video>
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## Training & Evaluation
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The model training is based on the **[LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT)**.
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For deployment, refer to **SGLang deployment** section in LLaVA-NeXT repo.
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For benchmark evaluation, the awesome **lmms-eval** package is used. Check our repo **[MultiUI](https://github.com/neulab/multiui)** to evaluate on benchmarks mentioned in the paper.
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## Model Performance
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/h1L7J4rLlq6EOtbiXZjZW.png)
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