diff --git a/.github/workflows/models.yaml b/.github/workflows/models.yaml
index b1433bb4..59a30da2 100644
--- a/.github/workflows/models.yaml
+++ b/.github/workflows/models.yaml
@@ -21,6 +21,7 @@ jobs:
           python-version: '3.10'
       - run: |
           python -m pip install --upgrade pip
-          pip install torch torchvision boto3
+          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+          pip install boto3 ultralytics
       - run: |
           python tools/convert-models.py
diff --git a/tools/convert-models.py b/tools/convert-models.py
index 639c9fd2..5d82677f 100644
--- a/tools/convert-models.py
+++ b/tools/convert-models.py
@@ -3,6 +3,7 @@
 import os
 import boto3
 from botocore.exceptions import ClientError
+from ultralytics import settings
 
 
 def upload_blob(bucket_name, source_file_name, destination_blob_name):
@@ -110,9 +111,34 @@ def blob_exist(bucket_name, blob_name):
   'fasterrcnn_resnet50_v2': 'https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth',
   'fasterrcnn_mobilenet_v3_large': 'https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth',
   'fasterrcnn_mobilenet_v3_large_320': 'https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth',
+  'yolo_v8_l': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt',
+  'yolo_v8_l_seg': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l-seg.pt',
+  'yolo_v8_m': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m.pt',
+  'yolo_v8_m_seg': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8m-seg.pt',
+  'yolo_v8_n': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt',
+  'yolo_v8_s': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s.pt',
+  'yolo_v8_s_seg': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8s-seg.pt',
+  'yolo_v8_x': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8x.pt',
+  'yolo_v11_l': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt',
+  'yolo_v11_m': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt',
+  'yolo_v11_n': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt',
+  'yolo_v11_s': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt',
+  'yolo_v11_s_cls': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt',
+  'yolo_v11_s_obb': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt',
+  'yolo_v11_s_pose': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt',
+  'yolo_v11_s_seg': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt',
+  'yolo_v11_x': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt',
+  'yolo_v12_l': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12l.pt',
+  'yolo_v12_m': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12m.pt',
+  'yolo_v12_n': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12n.pt',
+  'yolo_v12_s': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12s.pt',
+  'yolo_v12_x': 'https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo12x.pt',
   }
 
 os.makedirs("models", exist_ok=True)
+# yolo specifics 
+os.makedirs("runs", exist_ok=True)
+settings.update({"runs_dir": "runs/", "weights_dir": "models/", "sync": False})
 
 for name, url in models.items():
   fpath = "models/" + name + ".pth"
@@ -124,6 +150,11 @@ def blob_exist(bucket_name, blob_name):
     # download from url, convert and upload the converted weights
     m = load_state_dict_from_url(url, progress=False)
     converted = {}
+    
+    # yolo models weights are embedded in a BaseModel per https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/tasks.py#L309
+    if name.startswith("yolo_"):
+      m = m["model"].model.float().state_dict()
+    
     for nm, par in m.items():
       converted.update([(nm, par.clone())])
     torch.save(converted, fpath, _use_new_zipfile_serialization=True)