diff --git a/timm/models/_hub.py b/timm/models/_hub.py
index 91077f2b..c24b01cd 100644
--- a/timm/models/_hub.py
+++ b/timm/models/_hub.py
@@ -50,6 +50,8 @@ __all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'l
 # Default name for a weights file hosted on the Huggingface Hub.
 HF_WEIGHTS_NAME = "pytorch_model.bin"  # default pytorch pkl
 HF_SAFE_WEIGHTS_NAME = "model.safetensors"  # safetensors version
+HF_OPEN_CLIP_WEIGHTS_NAME = "open_clip_pytorch_model.bin"  # default pytorch pkl
+HF_OPEN_CLIP_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors"  # safetensors version
 
 
 def get_cache_dir(child_dir=''):
@@ -374,5 +376,7 @@ def _get_safe_alternatives(filename: str) -> Iterable[str]:
     """
     if filename == HF_WEIGHTS_NAME:
         yield HF_SAFE_WEIGHTS_NAME
-    if filename != HF_WEIGHTS_NAME and filename.endswith(".bin"):
-        return filename[:-4] + ".safetensors"
+    # if filename == HF_OPEN_CLIP_WEIGHTS_NAME:  # FIXME tracking safetensors yet
+    #     yield HF_OPEN_CLIP_SAFE_WEIGHTS_NAME
+    if filename not in (HF_WEIGHTS_NAME, HF_OPEN_CLIP_WEIGHTS_NAME) and filename.endswith(".bin"):
+        yield filename[:-4] + ".safetensors"
diff --git a/timm/models/eva.py b/timm/models/eva.py
index be7f3996..6142b2de 100644
--- a/timm/models/eva.py
+++ b/timm/models/eva.py
@@ -773,29 +773,51 @@ default_cfgs = generate_default_cfgs({
     ),
 
     # EVA01 and EVA02 CLIP image towers
-    'eva_giant_patch14_224.clip': _cfg(
-        #hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
+    'eva_giant_patch14_clip_224.laion400m': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
+        hf_hub_id='timm/eva_giant_patch14_clip_224.laion400m_s11b_b41k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
         num_classes=1024,
     ),
-    'eva02_base_patch16_clip_224.clip': _cfg(
-        #hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
+    'eva_giant_patch14_clip_224.merged2b': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
+        hf_hub_id='timm/eva_giant_patch14_plus_clip_224.merged2b_s11b_b114k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        num_classes=1024,
+    ),
+    'eva02_base_patch16_clip_224.merged2b': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
+        hf_hub_id='timm/eva02_base_patch16_clip_224.merged2b_s8b_b131k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
         num_classes=512,
     ),
-    'eva02_large_patch14_clip_224.clip': _cfg(
-        #hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
+    'eva02_large_patch14_clip_224.merged2b': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
+        hf_hub_id='timm/eva02_large_patch14_clip_224.merged2b_s4b_b131k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
         num_classes=768,
     ),
-    'eva02_large_patch14_clip_336.clip': _cfg(
+    'eva02_large_patch14_clip_336.merged2b': _cfg(
         # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
+        hf_hub_id='timm/eva02_large_patch14_clip_336.merged2b_s6b_b61k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
         input_size=(3, 336, 336), crop_pct=1.0,
         num_classes=768,
     ),
-    'eva02_enormous_patch14_clip_224.clip': _cfg(
-        #hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
+    'eva02_enormous_patch14_clip_224.laion2b': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
+        hf_hub_id='timm/eva02_enormous_patch14_clip_224.laion2b_s4b_b115k',  # float16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
+        num_classes=1024,
+    ),
+    'eva02_enormous_patch14_clip_224.laion2b_plus': _cfg(
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
+        hf_hub_id='timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k',  # bfloat16 weights
+        hf_hub_filename='open_clip_pytorch_model.bin',
         num_classes=1024,
     ),
     'eva02_enormous_patch14_clip_224.pretrain': _cfg(
-        #hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_E_psz14.pt',
+        # hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_E_psz14.pt',
         num_classes=0,
     ),
 
@@ -970,9 +992,19 @@ def eva02_large_patch14_448(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def eva_giant_patch14_clip_224(pretrained=False, **kwargs):
+    """ EVA-g CLIP model (only difference from non-CLIP is the pooling)  """
+    model_args = dict(
+        patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=6144 / 1408,
+        global_pool=kwargs.pop('global_pool', 'token'))
+    model = _create_eva('eva_giant_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def eva02_base_patch16_clip_224(pretrained=False, **kwargs):
-    # A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_base
+    """ A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_base """
     model_args = dict(
         img_size=224,
         patch_size=16,
@@ -986,7 +1018,7 @@ def eva02_base_patch16_clip_224(pretrained=False, **kwargs):
         scale_attn_inner=True,
         use_rot_pos_emb=True,
         ref_feat_shape=(16, 16),  # 224/14
-        global_pool='token',
+        global_pool=kwargs.pop('global_pool', 'token'),
     )
     model = _create_eva('eva02_base_patch16_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -994,7 +1026,7 @@ def eva02_base_patch16_clip_224(pretrained=False, **kwargs):
 
 @register_model
 def eva02_large_patch14_clip_224(pretrained=False, **kwargs):
-    # A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large
+    """ A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
     model_args = dict(
         img_size=224,
         patch_size=14,
@@ -1008,7 +1040,7 @@ def eva02_large_patch14_clip_224(pretrained=False, **kwargs):
         scale_attn_inner=True,
         use_rot_pos_emb=True,
         ref_feat_shape=(16, 16),  # 224/14
-        global_pool='token',
+        global_pool=kwargs.pop('global_pool', 'token'),
     )
     model = _create_eva('eva02_large_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -1016,7 +1048,7 @@ def eva02_large_patch14_clip_224(pretrained=False, **kwargs):
 
 @register_model
 def eva02_large_patch14_clip_336(pretrained=False, **kwargs):
-    # A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large
+    """ A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large """
     model_args = dict(
         img_size=336,
         patch_size=14,
@@ -1030,7 +1062,7 @@ def eva02_large_patch14_clip_336(pretrained=False, **kwargs):
         scale_attn_inner=True,
         use_rot_pos_emb=True,
         ref_feat_shape=(16, 16),  # 224/14
-        global_pool='token',
+        global_pool=kwargs.pop('global_pool', 'token'),
     )
     model = _create_eva('eva02_large_patch14_clip_336', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -1038,7 +1070,7 @@ def eva02_large_patch14_clip_336(pretrained=False, **kwargs):
 
 @register_model
 def eva02_enormous_patch14_clip_224(pretrained=False, **kwargs):
-    # A EVA-CLIP specific variant that uses residual post-norm in blocks
+    """ A EVA-CLIP specific variant that uses residual post-norm in blocks """
     model_args = dict(
         img_size=224,
         patch_size=14,
@@ -1047,7 +1079,7 @@ def eva02_enormous_patch14_clip_224(pretrained=False, **kwargs):
         num_heads=16,
         mlp_ratio=15360 / 1792,
         use_post_norm=True,
-        global_pool='token',
+        global_pool=kwargs.pop('global_pool', 'token'),
     )
     model = _create_eva('eva02_enormous_patch14_clip_224', pretrained=pretrained, **dict(model_args, **kwargs))
     return model