diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index bd9ea231..72f3a61a 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -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),
@@ -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