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@@ -27,7 +27,8 @@ It is _**the largest open-source vision/vision-language foundation model (14B)**
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  - **Model Stats:**
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  - Params (M): 5540 (the last 3 blocks are discarded)
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  - Image size: 448 x 448
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- - **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi, OCR data
 
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  - **Note:** InternViT-6B originally had 48 blocks, and we found that using the output after the fourth-to-last block worked best for VLLM. For ease of use and to save GPU memory, we simply discarded the last 3 blocks. Now, the model has only 45 blocks and the number of parameters has been reduced from 5.9B to 5.5B. Therefore, if you want to build a VLLM based on this model, **please make use of the features from the last layer.**
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  ## Model Usage (Image Embeddings)
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  - **Model Stats:**
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  - Params (M): 5540 (the last 3 blocks are discarded)
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  - Image size: 448 x 448
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+ - **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi, OCR data.
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+ To enhance the OCR capability of the model, we have incorporated additional OCR data alongside the general caption datasets. Specifically, we utilized PaddleOCR to perform Chinese OCR on images from Wukong and English OCR on images from LAION-COCO.
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  - **Note:** InternViT-6B originally had 48 blocks, and we found that using the output after the fourth-to-last block worked best for VLLM. For ease of use and to save GPU memory, we simply discarded the last 3 blocks. Now, the model has only 45 blocks and the number of parameters has been reduced from 5.9B to 5.5B. Therefore, if you want to build a VLLM based on this model, **please make use of the features from the last layer.**
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  ## Model Usage (Image Embeddings)
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