diff --git a/CHANGELOG.md b/CHANGELOG.md
index 16b74dab..133b0abc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@ Inspired from [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 - Add support for model connectors by @rawwar in ([#345](https://github.com/opensearch-project/opensearch-py-ml/pull/345))
 - Add support for model profiles by @rawwar in ([#358](https://github.com/opensearch-project/opensearch-py-ml/pull/358))
 - Support for security default admin credential changes in 2.12.0 in ([#365](https://github.com/opensearch-project/opensearch-py-ml/pull/365))
+- adding cross encoder models in the pre-trained traced list ([#378](https://github.com/opensearch-project/opensearch-py-ml/pull/378))
 
 
 ### Changed
diff --git a/utils/model_uploader/model_listing/pretrained_models_all_versions.json b/utils/model_uploader/model_listing/pretrained_models_all_versions.json
index 08e5bd19..1ac4c9d0 100644
--- a/utils/model_uploader/model_listing/pretrained_models_all_versions.json
+++ b/utils/model_uploader/model_listing/pretrained_models_all_versions.json
@@ -146,5 +146,30 @@
         ],
         "description": "This is a neural sparse encoding model: It transfers text into sparse vector, and then extract nonzero index and value to entry and weights. It serves only in ingestion and customer should use tokenizer model in query."
       }
-    },
+    }
+  },
+  {
+    "name": "huggingface/cross-encoders/ms-marco-MiniLM-L-6-v2",
+    "versions": {
+      "1.0.0": {
+        "format": [
+          "onnx",
+          "torch_script"
+        ],
+        "description": "The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order."
+      }
+    }
+  },
+  {
+    "name": "huggingface/cross-encoders/ms-marco-MiniLM-L-12-v2",
+    "versions": {
+      "1.0.0": {
+        "format": [
+          "onnx",
+          "torch_script"
+        ],
+        "description": "The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order."
+      }
+    }
+  }
 ]
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