diff --git a/crawl4ai/model_loader.py b/crawl4ai/model_loader.py
index 832b4240..5ff3695e 100644
--- a/crawl4ai/model_loader.py
+++ b/crawl4ai/model_loader.py
@@ -2,6 +2,7 @@ from functools import lru_cache
 from pathlib import Path
 import subprocess, os
 import shutil
+import tarfile
 from crawl4ai.config import MODEL_REPO_BRANCH
 import argparse
 import urllib.request
@@ -82,12 +83,19 @@ def load_bge_small_en_v1_5():
 @lru_cache()
 def load_onnx_all_MiniLM_l6_v2():
     from crawl4ai.onnx_embedding import DefaultEmbeddingModel
-    model_path = "models/onnx/model.onnx"
-    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/model.onnx"
-    download_path = os.path.join(__location__, model_path)
 
+    model_path = "models/onnx.tar.gz"
+    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/onnx.tar.gz"
+    __location__ = os.path.realpath(
+        os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    download_path = os.path.join(__location__, model_path)
+    onnx_dir = os.path.join(__location__, "models/onnx")
+    
+    # Create the models directory if it does not exist
+    os.makedirs(os.path.dirname(download_path), exist_ok=True)
+
+    # Download the tar.gz file if it does not exist
     if not os.path.exists(download_path):
-        # Define a download function with a simple progress display
         def download_with_progress(url, filename):
             def reporthook(block_num, block_size, total_size):
                 downloaded = block_num * block_size
@@ -95,12 +103,22 @@ def load_onnx_all_MiniLM_l6_v2():
                 if downloaded < total_size:
                     print(f"\rDownloading: {percentage:.2f}% ({downloaded / (1024 * 1024):.2f} MB of {total_size / (1024 * 1024):.2f} MB)", end='')
                 else:
-                    print("\rDownload complete!                              ")
+                    print("\rDownload complete!")
 
             urllib.request.urlretrieve(url, filename, reporthook)
 
         download_with_progress(model_url, download_path)
 
+    # Extract the tar.gz file if the onnx directory does not exist
+    if not os.path.exists(onnx_dir):
+        with tarfile.open(download_path, "r:gz") as tar:
+            tar.extractall(path=os.path.join(__location__, "models"))
+        
+        # remove the tar.gz file
+        os.remove(download_path)
+    
+    
+    
     model = DefaultEmbeddingModel()
     return model