diff --git a/tests/test_cuda.py b/tests/test_cuda.py
index a32220b106..df92d8212c 100644
--- a/tests/test_cuda.py
+++ b/tests/test_cuda.py
@@ -71,7 +71,7 @@ def test_predict_sam():
     predictor = SAMPredictor(overrides=overrides)
 
     # Set image
-    predictor.set_image('ultralytics/assets/zidane.jpg')  # set with image file
+    predictor.set_image(ASSETS / 'zidane.jpg')  # set with image file
     # predictor(bboxes=[439, 437, 524, 709])
     # predictor(points=[900, 370], labels=[1])
 
diff --git a/tests/test_python.py b/tests/test_python.py
index 98ee4de0d4..ad3ed096ba 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -140,11 +140,11 @@ def test_track_stream():
 
     # Test Global Motion Compensation (GMC) methods
     for gmc in 'orb', 'sift', 'ecc':
-        with open(ROOT / 'cfg/trackers/botsort.yaml') as f:
+        with open(ROOT / 'cfg/trackers/botsort.yaml', encoding='utf-8') as f:
             data = yaml.safe_load(f)
         tracker = TMP / f'botsort-{gmc}.yaml'
         data['gmc_method'] = gmc
-        with open(tracker, 'w') as f:
+        with open(tracker, 'w', encoding='utf-8') as f:
             yaml.safe_dump(data, f)
         model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker=tracker)
 
@@ -166,7 +166,7 @@ def test_train_pretrained():
 
 
 def test_export_torchscript():
-    f = YOLO(MODEL).export(format='torchscript', optimize=True)
+    f = YOLO(MODEL).export(format='torchscript', optimize=False)
     YOLO(f)(SOURCE)  # exported model inference
 
 
diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py
index 36222c862f..a1c2712e18 100644
--- a/ultralytics/models/fastsam/predict.py
+++ b/ultralytics/models/fastsam/predict.py
@@ -22,7 +22,7 @@ class FastSAMPredictor(DetectionPredictor):
                                     max_det=self.args.max_det,
                                     nc=len(self.model.names),
                                     classes=self.args.classes)
-        full_box = torch.zeros(p[0].shape[1])
+        full_box = torch.zeros(p[0].shape[1], device=p[0].device)
         full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0
         full_box = full_box.view(1, -1)
         critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:])
diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py
index 4c06a27a2f..4cedf765b1 100644
--- a/ultralytics/utils/__init__.py
+++ b/ultralytics/utils/__init__.py
@@ -311,7 +311,7 @@ def yaml_save(file='data.yaml', data=None, header=''):
             data[k] = str(v)
 
     # Dump data to file in YAML format
-    with open(file, 'w') as f:
+    with open(file, 'w', errors='ignore', encoding='utf-8') as f:
         if header:
             f.write(header)
         yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True)