Update README.md with new model card content

README.md

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 library_name: keras-hub
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-This is a [`DeiT` model](https://keras.io/api/keras_hub/models/dei_t) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-This model is related to a `ImageClassifier` task.
-
-Model config:
-* **name:** dei_t_backbone
-* **trainable:** True
-* **image_shape:** [224, 224, 3]
-* **patch_size:** [16, 16]
-* **num_layers:** 12
-* **num_heads:** 12
-* **hidden_dim:** 768
-* **intermediate_dim:** 3072
-* **dropout_rate:** 0.0
-* **attention_dropout:** 0.0
-* **layer_norm_epsilon:** 1e-12
-* **use_mha_bias:** True
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+Data-efficient Image Transformer (DeiT).
+
+**Reference**
+
+- [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)
+
+ViT models required training on expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.
+
+## Links
+
+* [DeiT Quickstart Notebook] - coming soon
+* [DeiT API Documentation] - coming soon
+* [DeiT Beginner Guide] - coming soon
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras
+```
+
+Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co. Full code examples for each are available below.
+
+| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
+|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| deit_tiny_distilled_patch16_224_imagenet | 5.52M      | DeiT-T16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
+| deit_small_distilled_patch16_224_imagenet | 21.66M      | DeiT-S16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
+| deit_base_distilled_patch16_224_imagenet | 85.80M      | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 . |
+| deit_base_distilled_patch16_384_imagenet | 86.09M     | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 384x384 |
+