Register MedGemma, MedSigLIP Presets to kerashub (#2450)

* Register MedGemma, MedSigLIP Presets to kerashub

* Address gemini comments

* Add siglip preset mapping

Author: laxmareddyp

keras_hub/src/models/gemma3/gemma3_presets.py

@@ -181,4 +181,43 @@
         },
         "kaggle_handle": "kaggle://keras/gemma3/keras/gemma3_instruct_270m/4",
     },
+    "medgemma_instruct_4b": {
+        "metadata": {
+            "description": (
+                "A 4 billion parameter model based on Gemma 3. "
+                "This model is trained for performance on medical text"
+                "and image comprehension and is optimized for medical"
+                "applications that involve a text generation component."
+            ),
+            "params": 4300079472,
+            "path": "gemma3",
+        },
+        "kaggle_handle": "kaggle://keras/medgemma/keras/medgemma_instruct_4b/1",
+    },
+    "medgemma_instruct_27b": {
+        "metadata": {
+            "description": (
+                "A 27 billion parameter model based on Gemma 3. "
+                "This model trained for performance on medical text "
+                "and image comprehension and is optimized for medical "
+                "applications that involve a text generation component."
+            ),
+            "params": 27432406640,
+            "path": "gemma3",
+        },
+        "kaggle_handle": "kaggle://keras/medgemma/keras/medgemma_instruct_27b/1",
+    },
+    "medgemma_instruct_27b_text": {
+        "metadata": {
+            "description": (
+                "A 27 billion parameter text-only model based on Gemma 3. "
+                "This model is trained for performance on medical text "
+                "comprehension and is optimized for medical applications "
+                "that involve a text generation component."
+            ),
+            "params": 27009002240,
+            "path": "gemma3",
+        },
+        "kaggle_handle": "kaggle://keras/medgemma/keras/medgemma_instruct_27b_text/1",
+    },
 }

keras_hub/src/models/siglip/siglip_presets.py

@@ -321,4 +321,19 @@
         },
         "kaggle_handle": "kaggle://keras/siglip/keras/siglip2_so400m_patch16_512/1",
     },
+    "medsiglip_900m_448": {
+        "metadata": {
+            "description": (
+                "A 900 million parameter variant of SigLIP trained to encode "
+                "medical images and text into a common embedding space. "
+                "MedSigLIP contains a vision encoder and a text encoder, and "
+                "supports 448x448 image resolution with up to 64 text tokens."
+            ),
+            "params": 878301426,
+            "official_name": "SigLIP2",
+            "path": "siglip",
+            "model_card": "https://huggingface.co/google/medsiglip-448#medsiglip-model-card",
+        },
+        "kaggle_handle": "kaggle://keras/medsiglip/keras/medsiglip_900m_448/1",
+    },
 }

tools/checkpoint_conversion/convert_siglip_checkpoints.py

@@ -110,6 +110,7 @@
     "siglip2_so400m_patch16_256": "google/siglip2-so400m-patch16-256",
     "siglip2_so400m_patch16_384": "google/siglip2-so400m-patch16-384",
     "siglip2_so400m_patch16_512": "google/siglip2-so400m-patch16-512",
+    "medsiglip_900m_448": "google/medsiglip-448",
 }
 
 flags.DEFINE_string(