Update README.md with new model card content

README.md

@@ -11,7 +11,7 @@ Weights are released under the [Apache 2 License](https://github.com/keras-team/
 ## Links
 
 * [SigLIP Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/siglip-quickstart-notebook-with-hub)
-* [SigLIP API Documentation](coming soon)
+* [SigLIP API Documentation](https://keras.io/keras_hub/api/models/siglip/)
 * [SigLIP Model Card](https://arxiv.org/abs/2303.15343)
 * [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
 * [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
@@ -33,7 +33,30 @@ The following model checkpoints are provided by the Keras team. Full code exampl
 
 | Preset name                            | Parameters | Description                                                                                                  |
 |---------------------------------------|------------|--------------------------------------------------------------------------------------------------------------|
-|   |   |  |
+| siglip_base_patch16_224 | 	203.16M	|  200 million parameter, image size 224, pre-trained on WebLi.  |  
+siglip_base_patch16_256 |	203.20M	 | 200 million parameter, image size 256, pre-trained on WebLi. |
+siglip_base_patch16_384 |	203.45M	|  200 million parameter, image size 384, pre-trained on WebLi. |
+siglip_base_patch16_512 |	203.79M	|  200 million parameter, image size 512, pre-trained on WebLi. |
+siglip_base_patch16_256_multilingual	|370.63M | 370 million parameter, image size 256, pre-trained on WebLi.|
+siglip2_base_patch16_224 |	375.19M	| 375 million parameter, patch size 16, image size 224, pre-trained on WebLi.|
+siglip2_base_patch16_256| 375.23M |	375 million parameter, patch size 16, image size 256, pre-trained on WebLi.|
+siglip2_base_patch32_256| 376.86M	| 376 million parameter, patch size 32, image size 256, pre-trained on WebLi.|
+siglip2_base_patch16_384 |	376.86M	| 376 million parameter, patch size 16, image size 384, pre-trained on WebLi.|
+siglip_large_patch16_256 |	652.15M	| 652 million parameter, image size 256, pre-trained on WebLi. |
+siglip_large_patch16_384 |	652.48M	| 652 million parameter, image size 384, pre-trained on WebLi. |
+siglip_so400m_patch14_224	| 877.36M	| 877 million parameter, image size 224, shape-optimized version, pre-trained on WebLi.|
+siglip_so400m_patch14_384	| 877.96M| 877 million parameter, image size 384, shape-optimized version, pre-trained on WebLi.|
+siglip2_large_patch16_256	|881.53M	|881 million parameter, patch size 16, image size 256, pre-trained on WebLi.|
+siglip2_large_patch16_384 |	881.86M	| 881 million parameter, patch size 16, image size 384, pre-trained on WebLi.|
+siglip2_large_patch16_512	| 882.31M	|882 million parameter, patch size 16, image size 512, pre-trained on WebLi.|
+siglip_so400m_patch16_256_i18n	| 1.13B	|1.1 billion parameter, image size 256, shape-optimized version, pre-trained on WebLi.|
+siglip2_so400m_patch14_224 |	1.14B	|1.1 billion parameter, patch size 14, image size 224, shape-optimized version, pre-trained on WebLi.|
+siglip2_so400m_patch16_256|	1.14B	|1.1 billion parameter, patch size 16, image size 256, shape-optimized version, pre-trained on WebLi.|
+siglip2_so400m_patch14_384 |	1.14B	|1.1 billion parameter, patch size 14, image size 224, shape-optimized version, pre-trained on WebLi.|
+siglip2_so400m_patch16_384 |	1.14B	|1.1 billion parameter, patch size 16, image size 384, shape-optimized version, pre-trained on WebLi.|
+siglip2_so400m_patch16_512|	1.14B	|1.1 billion parameter, patch size 16, image size 512, shape-optimized version, pre-trained on WebLi.|
+siglip2_giant_opt_patch16_256|	1.87B	|1.8 billion parameter, patch size 16, image size 256, pre-trained on WebLi.|
+siglip2_giant_opt_patch16_384|	1.87B	|1.8 billion parameter, patch size 16, image size 384, pre-trained on WebLi.|
 
 ## Example Usage
 ```Python