diff --git a/eval_image_retrieval.py b/eval_image_retrieval.py
index e60c71d2b..999f8c900 100644
--- a/eval_image_retrieval.py
+++ b/eval_image_retrieval.py
@@ -132,7 +132,7 @@ def config_qimname(cfg, i):
         model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
         print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
     elif "xcit" in args.arch:
-        model = torch.hub.load('facebookresearch/xcit', args.arch, num_classes=0)
+        model = torch.hub.load('facebookresearch/xcit:main', args.arch, num_classes=0)
     elif args.arch in torchvision_models.__dict__.keys():
         model = torchvision_models.__dict__[args.arch](num_classes=0)
     else:
diff --git a/eval_knn.py b/eval_knn.py
index 2b9f0054f..fe99a2604 100644
--- a/eval_knn.py
+++ b/eval_knn.py
@@ -60,7 +60,7 @@ def extract_feature_pipeline(args):
         model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
         print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
     elif "xcit" in args.arch:
-        model = torch.hub.load('facebookresearch/xcit', args.arch, num_classes=0)
+        model = torch.hub.load('facebookresearch/xcit:main', args.arch, num_classes=0)
     elif args.arch in torchvision_models.__dict__.keys():
         model = torchvision_models.__dict__[args.arch](num_classes=0)
         model.fc = nn.Identity()
diff --git a/eval_linear.py b/eval_linear.py
index 81eb94fd0..cdef16b47 100644
--- a/eval_linear.py
+++ b/eval_linear.py
@@ -41,7 +41,7 @@ def eval_linear(args):
         embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens))
     # if the network is a XCiT
     elif "xcit" in args.arch:
-        model = torch.hub.load('facebookresearch/xcit', args.arch, num_classes=0)
+        model = torch.hub.load('facebookresearch/xcit:main', args.arch, num_classes=0)
         embed_dim = model.embed_dim
     # otherwise, we check if the architecture is in torchvision models
     elif args.arch in torchvision_models.__dict__.keys():
diff --git a/hubconf.py b/hubconf.py
index ef1cdeaed..3709271ed 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -99,7 +99,7 @@ def dino_xcit_small_12_p16(pretrained=True, **kwargs):
     """
     XCiT-Small-12/16 pre-trained with DINO.
     """
-    model = torch.hub.load('facebookresearch/xcit', "xcit_small_12_p16", num_classes=0, **kwargs)
+    model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p16", num_classes=0, **kwargs)
     if pretrained:
         state_dict = torch.hub.load_state_dict_from_url(
             url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p16_pretrain/dino_xcit_small_12_p16_pretrain.pth",
@@ -113,7 +113,7 @@ def dino_xcit_small_12_p8(pretrained=True, **kwargs):
     """
     XCiT-Small-12/8 pre-trained with DINO.
     """
-    model = torch.hub.load('facebookresearch/xcit', "xcit_small_12_p8", num_classes=0, **kwargs)
+    model = torch.hub.load('facebookresearch/xcit:main', "xcit_small_12_p8", num_classes=0, **kwargs)
     if pretrained:
         state_dict = torch.hub.load_state_dict_from_url(
             url="https://dl.fbaipublicfiles.com/dino/dino_xcit_small_12_p8_pretrain/dino_xcit_small_12_p8_pretrain.pth",
@@ -127,7 +127,7 @@ def dino_xcit_medium_24_p16(pretrained=True, **kwargs):
     """
     XCiT-Medium-24/16 pre-trained with DINO.
     """
-    model = torch.hub.load('facebookresearch/xcit', "xcit_medium_24_p16", num_classes=0, **kwargs)
+    model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p16", num_classes=0, **kwargs)
     if pretrained:
         state_dict = torch.hub.load_state_dict_from_url(
             url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth",
@@ -141,7 +141,7 @@ def dino_xcit_medium_24_p8(pretrained=True, **kwargs):
     """
     XCiT-Medium-24/8 pre-trained with DINO.
     """
-    model = torch.hub.load('facebookresearch/xcit', "xcit_medium_24_p8", num_classes=0, **kwargs)
+    model = torch.hub.load('facebookresearch/xcit:main', "xcit_medium_24_p8", num_classes=0, **kwargs)
     if pretrained:
         state_dict = torch.hub.load_state_dict_from_url(
             url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p8_pretrain/dino_xcit_medium_24_p8_pretrain.pth",
diff --git a/main_dino.py b/main_dino.py
index 8e1756edf..cade9873d 100644
--- a/main_dino.py
+++ b/main_dino.py
@@ -44,7 +44,7 @@ def get_args_parser():
     # Model parameters
     parser.add_argument('--arch', default='vit_small', type=str,
         choices=['vit_tiny', 'vit_small', 'vit_base', 'xcit', 'deit_tiny', 'deit_small'] \
-                + torchvision_archs + torch.hub.list("facebookresearch/xcit"),
+                + torchvision_archs + torch.hub.list("facebookresearch/xcit:main"),
         help="""Name of architecture to train. For quick experiments with ViTs,
         we recommend using vit_tiny or vit_small.""")
     parser.add_argument('--patch_size', default=16, type=int, help="""Size in pixels
@@ -166,10 +166,10 @@ def train_dino(args):
         teacher = vits.__dict__[args.arch](patch_size=args.patch_size)
         embed_dim = student.embed_dim
     # if the network is a XCiT
-    elif args.arch in torch.hub.list("facebookresearch/xcit"):
-        student = torch.hub.load('facebookresearch/xcit', args.arch,
+    elif args.arch in torch.hub.list("facebookresearch/xcit:main"):
+        student = torch.hub.load('facebookresearch/xcit:main', args.arch,
                                  pretrained=False, drop_path_rate=args.drop_path_rate)
-        teacher = torch.hub.load('facebookresearch/xcit', args.arch, pretrained=False)
+        teacher = torch.hub.load('facebookresearch/xcit:main', args.arch, pretrained=False)
         embed_dim = student.embed_dim
     # otherwise, we check if the architecture is in torchvision models
     elif args.arch in torchvision_models.__dict__.keys():