diff --git a/hubconf.py b/hubconf.py
new file mode 100644
index 0000000..7027729
--- /dev/null
+++ b/hubconf.py
@@ -0,0 +1,77 @@
+"""
+Allows to load pre-trained models from the `ibot` repository.
+
+Example:
+```python
+import torch
+import torch.hub
+
+model = torch.hub.load("bytedance/ibot", "vits_16")
+"""
+
+import torch
+import torch.hub
+import torch.nn as nn
+
+import models.swin_transformer as st
+import models.vision_transformer as vt
+
+URL = "https://lf3-nlp-opensource.bytetos.com/obj/nlp-opensource/archive/2022/ibot/"
+
+PTHS = dict(
+    vit_s16="vits_16/checkpoint_teacher.pth",
+    swint_7="swint_7/checkpoint_teacher.pth",
+    swint_14="swint_14/checkpoint_teacher.pth",
+    vitb_16="vitb_16/checkpoint_teacher.pth",
+    vitb_16_rand_mask="vitb_16_rand_mask/checkpoint_teacher.pth",
+    vitl_16="vitl_16/checkpoint_teacher.pth",
+    vitl_16_rand_mask="vitl_16_rand_mask/checkpoint_teacher.pth",
+)
+
+
+def _load_ckpt(pth, model: nn.Module, pretrained=True, **kwargs):
+    if pretrained:
+        pth = torch.hub.load_state_dict_from_url(
+            url=URL + pth, file_name="ibot_" + pth.split("/")[0]
+        )
+        state_dict = pth["state_dict"]
+        model.load_state_dict(state_dict, strict=False)
+    return model
+
+
+def vits_16(**kwargs):
+    model = vt.vit_small(**kwargs)
+    return _load_ckpt(PTHS["vit_s16"], model)
+
+
+def swint_7(**kwargs):
+    model = st.swin_tiny(**kwargs)
+    return _load_ckpt(PTHS["swint_7"], model)
+
+
+def swint_14(**kwargs):
+    model = st.swin_tiny(**kwargs, window_size=14)
+    return _load_ckpt(PTHS["swint_14"], model)
+
+
+def vitb_16(**kwargs):
+    model = vt.vit_base(**kwargs)
+    return _load_ckpt(PTHS["vitb_16"], model)
+
+
+def vitb_16_rand_mask(**kwargs):
+    model = vt.vit_base(**kwargs)
+    return _load_ckpt(PTHS["vitb_16_rand_mask"], model)
+
+
+def vitl_16(**kwargs):
+    model = vt.vit_large(**kwargs)
+    return _load_ckpt(PTHS["vitl_16"], model)
+
+
+def vitl_16_rand_mask(**kwargs):
+    model = vt.vit_large(**kwargs)
+    return _load_ckpt(PTHS["vitl_16_rand_mask"], model)
+
+
+dependencies = ["torch"]