diff --git a/CHANGELOG.md b/CHANGELOG.md
index b083ebd7a..93e93bfc2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -58,6 +58,7 @@ Inspired from [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 - Bump pandas from 1.5.3 to the latest stable version by @yerzhaisang ([#422](https://github.com/opensearch-project/opensearch-py-ml/pull/422))
 - Upgrade mypy, sphinx, sphinx-rtd-theme, and multiple GitHub Actions (setup-python, backport, codecov-action, create-pull-request, get-pr-commits) by @yerzhaisang([#437](https://github.com/opensearch-project/opensearch-py-ml/pull/437))
 - Increment version to 1.2.0 and update maintainer list in _version.py by @nathaliellenaa([#516](https://github.com/opensearch-project/opensearch-py-ml/pull/516))
+- Update pretrained_models_all_versions.json (2025-05-29 15:51:56) by @dhrubo-os ([#520](https://github.com/opensearch-project/opensearch-py-ml/pull/520))
 
 ### Fixed
 - Fix the wrong final zip file name in model_uploader workflow, now will name it by the upload_prefix alse.([#413](https://github.com/opensearch-project/opensearch-py-ml/pull/413/files))
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 bfcb73eb2..7b9ea0e58 100644
--- a/utils/model_uploader/model_listing/pretrained_models_all_versions.json
+++ b/utils/model_uploader/model_listing/pretrained_models_all_versions.json
@@ -1,122 +1,96 @@
 [
   {
-    "name": "huggingface/sentence-transformers/all-MiniLM-L12-v2",
+    "name": "huggingface/cross-encoders/ms-marco-MiniLM-L-12-v2",
     "versions": {
-      "1.0.1": {
+      "1.0.0": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
-      }
-    }
-  },
-  {
-    "name": "huggingface/sentence-transformers/all-MiniLM-L6-v2",
-    "versions": {
+        "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."
+      },
       "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
-      }
-    }
-  },
-  {
-    "name": "huggingface/sentence-transformers/all-distilroberta-v1",
-    "versions": {
-      "1.0.1": {
+        "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."
+      },
+      "1.0.2": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search."
+        "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/sentence-transformers/all-mpnet-base-v2",
+    "name": "huggingface/cross-encoders/ms-marco-MiniLM-L-6-v2",
     "versions": {
-      "1.0.1": {
+      "1.0.0": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search."
-      }
-    }
-  },
-  {
-    "name": "huggingface/sentence-transformers/distiluse-base-multilingual-cased-v1",
-    "versions": {
-      "1.0.1": {
-        "format": [
-          "torch_script"
-        ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search."
-      }
-    }
-  },
-  {
-    "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b",
-    "versions": {
+        "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."
+      },
       "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search."
+        "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."
       },
       "1.0.2": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search. The model version automatically truncates input to a maximum of 512 tokens."
+        "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/sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+    "name": "huggingface/sentence-transformers/all-MiniLM-L12-v2",
     "versions": {
       "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1",
+    "name": "huggingface/sentence-transformers/all-MiniLM-L6-v2",
     "versions": {
       "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2",
+    "name": "huggingface/sentence-transformers/all-distilroberta-v1",
     "versions": {
       "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "huggingface/sentence-transformers/paraphrase-mpnet-base-v2",
+    "name": "huggingface/sentence-transformers/all-mpnet-base-v2",
     "versions": {
-      "1.0.0": {
+      "1.0.1": {
         "format": [
           "onnx",
           "torch_script"
@@ -126,93 +100,92 @@
     }
   },
   {
-    "name": "huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+    "name": "huggingface/sentence-transformers/distiluse-base-multilingual-cased-v1",
     "versions": {
       "1.0.1": {
         "format": [
-          "onnx",
           "torch_script"
         ],
-        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1",
+    "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b",
     "versions": {
       "1.0.1": {
         "format": [
+          "onnx",
           "torch_script"
         ],
-        "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": {
+        "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search."
+      },
       "1.0.2": {
         "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."
+        "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search. This model version automatically truncates to a maximum of 512 tokens."
       }
     }
   },
   {
-    "name": "huggingface/cross-encoders/ms-marco-MiniLM-L-12-v2",
+    "name": "huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
     "versions": {
-      "1.0.2": {
+      "1.0.1": {
         "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."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources."
       }
     }
   },
   {
-    "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v2-distill",
+    "name": "huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1",
     "versions": {
-      "1.0.0": {
+      "1.0.1": {
         "format": [
+          "onnx",
           "torch_script"
         ],
-        "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."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources."
       }
     }
   },
   {
-    "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v2-mini",
+    "name": "huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2",
     "versions": {
-      "1.0.0": {
+      "1.0.1": {
         "format": [
+          "onnx",
           "torch_script"
         ],
-        "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."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-v2-distill",
+    "name": "huggingface/sentence-transformers/paraphrase-mpnet-base-v2",
     "versions": {
       "1.0.0": {
         "format": [
+          "onnx",
           "torch_script"
         ],
-        "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 in both ingestion and search."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   },
   {
-    "name": "amazon/sentence-highlighting/opensearch-semantic-highlighter-v1",
+    "name": "huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
     "versions": {
-      "1.0.0": {
+      "1.0.1": {
         "format": [
+          "onnx",
           "torch_script"
         ],
-        "description": "A semantic highlighter model that identifies and highlights relevant sentences in a document given a query."
+        "description": "This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search."
       }
     }
   }