diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index a633d98a..701fcb84 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -964,11 +964,13 @@ def _convert_openai_clip(
             v = v.unsqueeze(0)
             if v.shape[1] != model.pos_embed.shape[1]:
                 # To resize pos embedding when using model at different size from pretrained weights
-                v = resize_pos_embed(
+                num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) \
+                    else getattr(model, 'num_prefix_tokens', 1)
+                v = resample_abs_pos_embed(
                     v,
-                    model.pos_embed,
-                    0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
-                    model.patch_embed.grid_size
+                    new_size=model.patch_embed.grid_size,
+                    num_prefix_tokens=num_prefix_tokens,
+                    verbose=True,
                 )
         out_dict[k] = v
     return out_dict
@@ -1015,8 +1017,6 @@ def checkpoint_filter_fn(
         return _convert_openai_clip(state_dict, model)
     elif 'module.visual.class_embedding' in state_dict:
         return _convert_openai_clip(state_dict, model, prefix='module.visual.')
-    elif '_image_encoder.module.visual.class_embedding' in state_dict:
-        return _convert_openai_clip(state_dict, model, prefix='_image_encoder.module.visual.')
 
     if "mask_token" in state_dict:
         state_dict = _convert_dinov2(state_dict, model)
@@ -1737,20 +1737,24 @@ default_cfgs = {
         input_size=(3, 384, 384),
         num_classes=0),
 
-    'vit_8m_patch16_tinyclip_224.yfcc15m': _cfg(
-        url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M.pt',
+    'vit_xsmall_patch16_clip_224.tinyclip_yfcc15m': _cfg(
+        hf_hub_id='timm/',
+        hf_hub_filename='open_clip_pytorch_model.bin',
         license='mit',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
-    'vit_39m_patch16_tinyclip_224.yfcc15m': _cfg(
-        url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M.pt',
+    'vit_medium_patch32_clip_224.tinyclip_laion400m': _cfg(
+        hf_hub_id='timm/',
+        hf_hub_filename='open_clip_pytorch_model.bin',
         license='mit',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
-    'vit_40m_patch32_tinyclip_224.laion400m': _cfg(
-        url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-40M-32-Text-19M-LAION400M.pt',
+    'vit_medium_patch16_clip_224.tinyclip_yfcc15m': _cfg(
+        hf_hub_id='timm/',
+        hf_hub_filename='open_clip_pytorch_model.bin',
         license='mit',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
-    'vit_61m_patch32_tinyclip_224.laion400m': _cfg(
-        url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-61M-32-Text-29M-LAION400M.pt',
+    'vit_betwixt_patch32_clip_224.tinyclip_laion400m': _cfg(
+        hf_hub_id='timm/',
+        hf_hub_filename='open_clip_pytorch_model.bin',
         license='mit',
         mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
 
@@ -2092,6 +2096,44 @@ def vit_giant_patch16_gap_224(pretrained: bool = False, **kwargs) -> VisionTrans
     return model
 
 
+@register_model
+def vit_xsmall_patch16_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    # TinyCLIP 8M
+    model_args = dict(embed_dim=256, depth=10, num_heads=4, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_xsmall_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_medium_patch32_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    # TinyCLIP 40M
+    model_args = dict(
+        patch_size=32, embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_medium_patch32_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_medium_patch16_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    # TinyCLIP 39M
+    model_args = dict(embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_medium_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_betwixt_patch32_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
+    # TinyCLIP 61M
+    model_args = dict(
+        patch_size=32, embed_dim=640, depth=12, num_heads=10, pre_norm=True, norm_layer=nn.LayerNorm)
+    model = _create_vision_transformer(
+        'vit_betwixt_patch32_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_base_patch32_clip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
     """ ViT-B/32 CLIP image tower @ 224x224
@@ -2640,40 +2682,6 @@ def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionT
     return model
 
 
-@register_model
-def vit_8m_patch16_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
-    model_args = dict(embed_dim=256, depth=10, num_heads=4, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer(
-        'vit_8m_patch16_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
-@register_model
-def vit_39m_patch16_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
-    model_args = dict(embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer(
-        'vit_39m_patch16_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
-@register_model
-def vit_40m_patch32_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
-    model_args = dict(
-        patch_size=32, embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer(
-        'vit_40m_patch32_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
-@register_model
-def vit_61m_patch32_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
-    model_args = dict(
-        patch_size=32, embed_dim=640, depth=12, num_heads=10, pre_norm=True, norm_layer=nn.LayerNorm)
-    model = _create_vision_transformer(
-        'vit_61m_patch32_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
-    return model
-
-
 @register_model
 def vit_medium_patch16_reg4_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
     model_args = dict(