Update README.md with new model card content

README.md

@@ -15,7 +15,7 @@ T5 encoder-decoder backbone model.
 T5 is a LLM pretrained on a mix of unsupervised and supervised tasks,
 where each task is converted to a sequence-to-sequence format.
 T5 works well on a variety of tasks out-of-the-box by prepending
-various prefixex to the input sequence, e.g., for translation:
+various prefixes to the input sequence, e.g., for translation:
 `"translate English to German: ..."`, for summarization:
 `"summarize: ..."`.
 
@@ -29,6 +29,41 @@ preset architectures and weights, use the `from_preset` constructor.
 Disclaimer: Pre-trained models are provided on an "as is" basis, without
 warranties or conditions of any kind.
 
+## Links
+
+* [T5 Quickstart Notebook](coming soon)
+* [T5  API Documentation](https://keras.io/keras_hub/api/models/t5/)
+* [T5 Model Card](https://github.com/google-research/text-to-text-transfer-transformer/tree/main)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [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. Full code examples for each are available below.
+| Preset name    | Parameters | Description                                      |
+|----------------|------------|--------------------------------------------------|
+| t5_small_multi |   0    | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).|
+| t5_base_multi|    0  | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
+| t5_large_multi |   0   | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).   |
+| flan_small_multi |   0    |  8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).  |
+| flan_base_multi |   0    |  12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
+| flan_large_multi |   0    |  	24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).  |
+| t5_1.1_small |   60.51M |    |
+| tt5_1.1_base |  247.58M |    | 
+| t5_1.1_large | 750.25M  |    | 
+| t5_1.1_xl | 2.85B  |    | 
+| t5_1.1_xxl | 11.14B  |    | 
 
 __Arguments__