diff --git a/timm/models/maxxvit.py b/timm/models/maxxvit.py
index 2475e809..de2c9fb8 100644
--- a/timm/models/maxxvit.py
+++ b/timm/models/maxxvit.py
@@ -651,7 +651,11 @@ class LayerScale2d(nn.Module):
 
 
 class Downsample2d(nn.Module):
-    """ A downsample pooling module for Coat that handles 2d <-> 1d conversion
+    """ A downsample pooling module supporting several maxpool and avgpool modes
+    * 'max' - MaxPool2d w/ kernel_size 3, stride 2, padding 1
+    * 'max2' - MaxPool2d w/ kernel_size = stride = 2
+    * 'avg' - AvgPool2d w/ kernel_size 3, stride 2, padding 1
+    * 'avg2' - AvgPool2d w/ kernel_size = stride = 2
     """
 
     def __init__(
@@ -710,6 +714,11 @@ def _init_transformer(module, name, scheme=''):
 class TransformerBlock2d(nn.Module):
     """ Transformer block with 2D downsampling
     '2D' NCHW tensor layout
+
+    Some gains can be seen on GPU using a 1D / CL block, BUT w/ the need to switch back/forth to NCHW
+    for spatial pooling, the benefit is minimal so ended up using just this variant for CoAt configs.
+
+    This impl was faster on TPU w/ PT XLA than the 1D experiment.
     """
 
     def __init__(
@@ -1011,9 +1020,9 @@ def get_rel_pos_cls(cfg: MaxxVitTransformerCfg, window_size):
     return rel_pos_cls
 
 
-class PartitionAttention(nn.Module):
+class PartitionAttentionCl(nn.Module):
     """ Grid or Block partition + Attn + FFN.
-    NxC tensor layout.
+    NxC 'channels last' tensor layout.
     """
 
     def __init__(
@@ -1183,6 +1192,7 @@ def grid_reverse_nchw(windows, grid_size: List[int], img_size: List[int]):
 
 class PartitionAttention2d(nn.Module):
     """ Grid or Block partition + Attn + FFN
+
     '2D' NCHW tensor layout.
     """
 
@@ -1245,7 +1255,7 @@ class PartitionAttention2d(nn.Module):
 
 
 class MaxxVitBlock(nn.Module):
-    """
+    """ MaxVit conv, window partition + FFN , grid partition + FFN
     """
 
     def __init__(
@@ -1264,7 +1274,7 @@ class MaxxVitBlock(nn.Module):
         self.conv = conv_cls(dim, dim_out, stride=stride, cfg=conv_cfg, drop_path=drop_path)
 
         attn_kwargs = dict(dim=dim_out, cfg=transformer_cfg, drop_path=drop_path)
-        partition_layer = PartitionAttention2d if use_nchw_attn else PartitionAttention
+        partition_layer = PartitionAttention2d if use_nchw_attn else PartitionAttentionCl
         self.nchw_attn = use_nchw_attn
         self.attn_block = partition_layer(**attn_kwargs)
         self.attn_grid = partition_layer(partition_type='grid', **attn_kwargs)
@@ -1288,7 +1298,8 @@ class MaxxVitBlock(nn.Module):
 
 
 class ParallelMaxxVitBlock(nn.Module):
-    """
+    """ MaxVit block with parallel cat(window + grid), one FF
+    Experimental timm block.
     """
 
     def __init__(
@@ -1427,7 +1438,9 @@ class Stem(nn.Module):
 
 
 class MaxxVit(nn.Module):
-    """
+    """ CoaTNet + MaxVit base model.
+
+    Highly configurable for different block compositions, tensor layouts, pooling types.
     """
 
     def __init__(