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README.md
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@@ -28,6 +28,8 @@ Usually, in LLaVA models, we generate N embeddings for the image, which we then
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for one image, we create K<<N tokens for M<N parts of the image (crops)? It would allow us to get visual information from small parts of the image and not inflate the
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number of image "tokens" too much. I called this method multi-crop LLaVA (MC-LLaVA).
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MC-LLaVA-3b was fine-tuned from [Phi-2 merge](vince62s/phi-2-psy) using vision tower from
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[SigLIP 400M](https://huggingface.co/google/siglip-so400m-patch14-384).
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for one image, we create K<<N tokens for M<N parts of the image (crops)? It would allow us to get visual information from small parts of the image and not inflate the
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number of image "tokens" too much. I called this method multi-crop LLaVA (MC-LLaVA).
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You can read more about the model in the [blog post](https://huggingface.co/blog/visheratin/vlm-resolution-curse).
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MC-LLaVA-3b was fine-tuned from [Phi-2 merge](vince62s/phi-2-psy) using vision tower from
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[SigLIP 400M](https://huggingface.co/google/siglip-so400m-patch14-384).
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