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README.md

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 # Model Card for cerebras/Cerebras-LLaVA-13B
 The checkpoints consists of Language encoder and projector weights of multimodal LLaVA-13B model trained with our Cerebras implementation and training recipe. 
-The vision encoder checkpoints for this model can be found at [cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V](https://huggingface.co/cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V)
+The vision encoder checkpoints for this model can be found at [cerebras/Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V](https://huggingface.co/cerebras/Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V)
 
 **Note**: _ShareGPT4V_ is added to the vision model name to ensure correct loading of checkpoints in [LLaVA source repo](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/builder.py#L8)
 
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 ## Model Architecture
 Cerebras-LLaVA-13B is a transformer model with the following architecture details
-* Vision encoder: [CLIP-VisionModel-Large](cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V). It handles images of size 336 x 336 with patch size of 14
+* Vision encoder: [CLIP-VisionModel-Large](https://huggingface.co/cerebras/Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V)
+. It handles images of size 336 x 336 with patch size of 14
 * Large Language Model: Pretrained from Vicuna-13B checkpoints and instruction finetuned on various datasets.
 * Projector: the projector module that connects the LLM and Vision encoder part consists of two linear layers with gelu activation (mlp2x-gelu)