Update README.md

README.md

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 license: apache-2.0
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+# Model Card for cerebras/Cerebras-LLaVA-7B
+
+The checkpoints consists of Language encoder and projector weights of multimodal LLaVA-7B 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)
+
+**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)
+
+For full details of this model and training details, please read our paper and release blog post **to be released shortly**.
+
+# Model Architecture
+Cerebras-LLaVA-7B 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
+* Large Language Model: Pretrained from Vicuna-7B 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)
+
+# Loading the model
+
+This model can directly be loaded using the [LLaVa source code repository](https://github.com/haotian-liu/LLaVA). For installation, please refer to the [instructions in source code repository](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#install).
+
+```
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path
+from llava.eval.run_llava import eval_model
+
+model_path = "cerebras/Cerebras-LLaVA-7B"
+
+tokenizer, model, image_processor, context_len = load_pretrained_model(
+    model_path=model_path,
+    model_base=None,
+    model_name=get_model_name_from_path(model_path)
+)
+```
+
+# Acknowledgements
+We are thankful to all Cerebras engineers, past and present, that made this work possible.
+
+