diff --git a/tests/test_models.py b/tests/test_models.py
index c673dc96..db8efbf3 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -24,7 +24,7 @@ MAX_FWD_FEAT_SIZE = 448
 
 
 @pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-1]))
 @pytest.mark.parametrize('batch_size', [1])
 def test_model_forward(model_name, batch_size):
     """Run a single forward pass with each model"""
diff --git a/timm/models/helpers.py b/timm/models/helpers.py
index ac119295..b90ce1db 100644
--- a/timm/models/helpers.py
+++ b/timm/models/helpers.py
@@ -277,11 +277,12 @@ def build_model_with_cfg(
     if pruned:
         model = adapt_model_from_file(model, variant)
 
+    # for classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
+    num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
     if pretrained:
         load_pretrained(
             model,
-            num_classes=kwargs.get('num_classes', 0),
-            in_chans=kwargs.get('in_chans', 3),
+            num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3),
             filter_fn=pretrained_filter_fn, strict=pretrained_strict)
     
     if features:
diff --git a/timm/models/hrnet.py b/timm/models/hrnet.py
index 1e867686..2e8757b5 100644
--- a/timm/models/hrnet.py
+++ b/timm/models/hrnet.py
@@ -776,6 +776,7 @@ def _create_hrnet(variant, pretrained, **model_kwargs):
     strict = True
     if model_kwargs.pop('features_only', False):
         model_cls = HighResolutionNetFeatures
+        model_kwargs['num_classes'] = 0
         strict = False
 
     return build_model_with_cfg(
diff --git a/timm/models/layers/create_act.py b/timm/models/layers/create_act.py
index 5bc4db99..6f2ab83e 100644
--- a/timm/models/layers/create_act.py
+++ b/timm/models/layers/create_act.py
@@ -6,9 +6,14 @@ from .activations_jit import *
 from .activations_me import *
 from .config import is_exportable, is_scriptable, is_no_jit
 
+# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7. This code
+# will use native version if present. Eventually, the custom Swish layers will be removed
+# and only native 'silu' will be used.
+_has_silu = 'silu' in dir(torch.nn.functional)
 
 _ACT_FN_DEFAULT = dict(
-    swish=swish,
+    silu=F.silu if _has_silu else swish,
+    swish=F.silu if _has_silu else swish,
     mish=mish,
     relu=F.relu,
     relu6=F.relu6,
@@ -26,7 +31,8 @@ _ACT_FN_DEFAULT = dict(
 )
 
 _ACT_FN_JIT = dict(
-    swish=swish_jit,
+    silu=F.silu if _has_silu else swish_jit,
+    swish=F.silu if _has_silu else swish_jit,
     mish=mish_jit,
     hard_sigmoid=hard_sigmoid_jit,
     hard_swish=hard_swish_jit,
@@ -34,7 +40,8 @@ _ACT_FN_JIT = dict(
 )
 
 _ACT_FN_ME = dict(
-    swish=swish_me,
+    silu=F.silu if _has_silu else swish_me,
+    swish=F.silu if _has_silu else swish_me,
     mish=mish_me,
     hard_sigmoid=hard_sigmoid_me,
     hard_swish=hard_swish_me,
@@ -42,7 +49,8 @@ _ACT_FN_ME = dict(
 )
 
 _ACT_LAYER_DEFAULT = dict(
-    swish=Swish,
+    silu=nn.SiLU if _has_silu else Swish,
+    swish=nn.SiLU if _has_silu else Swish,
     mish=Mish,
     relu=nn.ReLU,
     relu6=nn.ReLU6,
@@ -60,7 +68,8 @@ _ACT_LAYER_DEFAULT = dict(
 )
 
 _ACT_LAYER_JIT = dict(
-    swish=SwishJit,
+    silu=nn.SiLU if _has_silu else SwishJit,
+    swish=nn.SiLU if _has_silu else SwishJit,
     mish=MishJit,
     hard_sigmoid=HardSigmoidJit,
     hard_swish=HardSwishJit,
@@ -68,7 +77,8 @@ _ACT_LAYER_JIT = dict(
 )
 
 _ACT_LAYER_ME = dict(
-    swish=SwishMe,
+    silu=nn.SiLU if _has_silu else SwishMe,
+    swish=nn.SiLU if _has_silu else SwishMe,
     mish=MishMe,
     hard_sigmoid=HardSigmoidMe,
     hard_swish=HardSwishMe,
diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 042efc05..72f3a61a 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -37,7 +37,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
         'crop_pct': .9, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': '', 'classifier': 'head',
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
         **kwargs
     }
 
@@ -48,7 +48,8 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
     ),
     'vit_base_patch16_224': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_base_p16_224-4e355ebd.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
     ),
     'vit_base_patch16_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
@@ -56,7 +57,9 @@ default_cfgs = {
     'vit_base_patch32_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
-    'vit_large_patch16_224': _cfg(),
+    'vit_large_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
     'vit_large_patch16_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
         input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
@@ -206,7 +209,7 @@ class VisionTransformer(nn.Module):
                  drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm):
         super().__init__()
         self.num_classes = num_classes
-        self.embed_dim = embed_dim
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
 
         if hybrid_backbone is not None:
             self.patch_embed = HybridEmbed(
@@ -305,10 +308,9 @@ def vit_small_patch16_224(pretrained=False, **kwargs):
 
 @register_model
 def vit_base_patch16_224(pretrained=False, **kwargs):
-    if pretrained:
-        # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
-        kwargs.setdefault('qk_scale', 768 ** -0.5)
-    model = VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
+    model = VisionTransformer(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
     model.default_cfg = default_cfgs['vit_base_patch16_224']
     if pretrained:
         load_pretrained(
@@ -340,8 +342,12 @@ def vit_base_patch32_384(pretrained=False, **kwargs):
 
 @register_model
 def vit_large_patch16_224(pretrained=False, **kwargs):
-    model = VisionTransformer(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
+    model = VisionTransformer(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
     model.default_cfg = default_cfgs['vit_large_patch16_224']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
     return model
 
 
diff --git a/timm/version.py b/timm/version.py
index 020ed73d..0404d810 100644
--- a/timm/version.py
+++ b/timm/version.py
@@ -1 +1 @@
-__version__ = '0.2.2'
+__version__ = '0.3.0'