diff --git a/curated_transformers/layers/activations.py b/curated_transformers/layers/activations.py
index d3503133..4867a77e 100644
--- a/curated_transformers/layers/activations.py
+++ b/curated_transformers/layers/activations.py
@@ -1,5 +1,5 @@
 import math
-from enum import Enum, EnumMeta
+from enum import Enum
 from typing import Type
 
 import torch
@@ -7,46 +7,7 @@
 from torch.nn import Module
 
 
-class _ActivationMeta(EnumMeta):
-    """
-    ``Enum`` metaclass to override the class ``__call__`` method with a more
-    fine-grained exception for unknown activation functions.
-    """
-
-    def __call__(
-        cls,
-        value,
-        names=None,
-        *,
-        module=None,
-        qualname=None,
-        type=None,
-        start=1,
-    ):
-        # Wrap superclass __call__ to give a nicer error message when
-        # an unknown activation is used.
-        if names is None:
-            try:
-                return EnumMeta.__call__(
-                    cls,
-                    value,
-                    names,
-                    module=module,
-                    qualname=qualname,
-                    type=type,
-                    start=start,
-                )
-            except ValueError:
-                supported_activations = ", ".join(sorted(v.value for v in cls))
-                raise ValueError(
-                    f"Invalid activation function `{value}`. "
-                    f"Supported functions: {supported_activations}"
-                )
-        else:
-            return EnumMeta.__call__(cls, value, names, module, qualname, type, start)
-
-
-class Activation(Enum, metaclass=_ActivationMeta):
+class Activation(Enum):
     """
     Activation functions.
 
@@ -71,6 +32,14 @@ class Activation(Enum, metaclass=_ActivationMeta):
     #: Sigmoid Linear Unit (`Hendrycks et al., 2016`_).
     SiLU = "silu"
 
+    @classmethod
+    def _missing_(cls, value):
+        supported_activations = ", ".join(sorted(v.value for v in cls))
+        raise ValueError(
+            f"Invalid activation function `{value}`. "
+            f"Supported functions: {supported_activations}"
+        )
+
     @property
     def module(self) -> Type[torch.nn.Module]:
         """
diff --git a/curated_transformers/tests/layers/test_embeddings.py b/curated_transformers/tests/layers/test_embeddings.py
index 085db7ad..8e741110 100644
--- a/curated_transformers/tests/layers/test_embeddings.py
+++ b/curated_transformers/tests/layers/test_embeddings.py
@@ -24,7 +24,8 @@ def test_rotary_embeddings_against_hf(device):
 
     X = torch.rand(16, 12, 64, 768, device=device)
     Y = re(X)
-    hf_re_cos, hf_re_sin = hf_re(X, seq_len=X.shape[-2])
+    positions = torch.arange(X.shape[2], device=device).view([1, -1])
+    hf_re_cos, hf_re_sin = hf_re(X, positions)
     Y_hf = hf_re_cos * X + hf_re_sin * rotate_half(X)
 
     torch_assertclose(Y, Y_hf)
diff --git a/curated_transformers/tests/tokenizers/test_hf_hub.py b/curated_transformers/tests/tokenizers/test_hf_hub.py
index 183145e4..b6859dce 100644
--- a/curated_transformers/tests/tokenizers/test_hf_hub.py
+++ b/curated_transformers/tests/tokenizers/test_hf_hub.py
@@ -51,6 +51,7 @@ def test_from_hf_hub_to_cache_legacy():
         )
 
 
+@pytest.mark.xfail(reason="HfFileSystem calls safetensors with incorrect arguments")
 @pytest.mark.skipif(not has_hf_transformers, reason="requires huggingface transformers")
 def test_fsspec(sample_texts):
     # We only test one model, since using fsspec downloads the model
diff --git a/setup.cfg b/setup.cfg
index 52b0d016..654d45a8 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [metadata]
-version = 1.3.1
+version = 1.3.2
 description = A PyTorch library of transformer models and components
 url = https://github.com/explosion/curated-transformers
 author = Explosion