diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
index 99af178c..1225e10a 100644
--- a/timm/models/vision_transformer.py
+++ b/timm/models/vision_transformer.py
@@ -470,7 +470,6 @@ class VisionTransformer(nn.Module):
             reg_tokens: int = 0,
             pre_norm: bool = False,
             fc_norm: Optional[bool] = None,
-            use_attn_pool: bool = False,
             dynamic_img_size: bool = False,
             dynamic_img_pad: bool = False,
             drop_rate: float = 0.,
@@ -514,8 +513,8 @@ class VisionTransformer(nn.Module):
             block_fn: Transformer block layer.
         """
         super().__init__()
-        assert global_pool in ('', 'avg', 'token')
-        assert class_token or use_attn_pool or global_pool != 'token'
+        assert global_pool in ('', 'avg', 'token', 'map')
+        assert class_token or global_pool != 'token'
         use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
         act_layer = act_layer or nn.GELU
@@ -580,7 +579,7 @@ class VisionTransformer(nn.Module):
         self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
 
         # Classifier Head
-        if use_attn_pool == 'map':
+        if global_pool == 'pool':
             self.attn_pool = AttentionPoolLatent(
                 self.embed_dim,
                 num_heads=num_heads,
@@ -2243,7 +2242,7 @@ def vit_gigantic_patch16_224_ijepa(pretrained=False, **kwargs) -> VisionTransfor
 @register_model
 def vit_base_patch16_siglip_224(pretrained=False, **kwargs) -> VisionTransformer:
     model_args = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, use_attn_pool=True,
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, class_token=False, global_pool='map',
     )
     model = _create_vision_transformer(
         'vit_base_patch16_siglip_224', pretrained=pretrained, **dict(model_args, **kwargs))