diff --git a/README.md b/README.md
index a282b20..6871207 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ A full EGNN network
 
 ```python
 import torch
-from egnn_pytorch.egnn_pytorch import EGNN_Network
+from egnn_pytorch import EGNN_Network
 
 net = EGNN_Network(
     num_tokens = 21,
@@ -55,7 +55,7 @@ net = EGNN_Network(
     dim = 32,
     depth = 3,
     num_nearest_neighbors = 8,
-    coor_weights_clamp_value = 2.   # absolute clampd value for the coordinate weights, needed if you increase the num neareest neighbors
+    coor_weights_clamp_value = 2.   # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
 )
 
 feats = torch.randint(0, 21, (1, 1024)) # (1, 1024)
@@ -69,7 +69,7 @@ Only attend to sparse neighbors, given to the network as an adjacency matrix.
 
 ```python
 import torch
-from egnn_pytorch.egnn_pytorch import EGNN_Network
+from egnn_pytorch import EGNN_Network
 
 net = EGNN_Network(
     num_tokens = 21,
@@ -94,7 +94,7 @@ You can also have the network automatically determine the Nth-order neighbors, a
 
 ```python
 import torch
-from egnn_pytorch.egnn_pytorch import EGNN_Network
+from egnn_pytorch import EGNN_Network
 
 net = EGNN_Network(
     num_tokens = 21,
@@ -123,7 +123,7 @@ If you need to pass in continuous edges
 
 ```python
 import torch
-from egnn_pytorch.egnn_pytorch import EGNN_Network
+from egnn_pytorch import EGNN_Network
 
 net = EGNN_Network(
     num_tokens = 21,
@@ -147,6 +147,30 @@ adj_mat = (i[:, None] >= (i[None, :] - 1)) & (i[:, None] <= (i[None, :] + 1))
 feats_out, coors_out = net(feats, coors, edges = continuous_edges, mask = mask, adj_mat = adj_mat) # (1, 1024, 32), (1, 1024, 3)
 ```
 
+## Stability
+
+The initial architecture for EGNN suffered from instability when there was high number of neighbors. Thankfully, there seems to be two solutions that largely mitigate this.
+
+```python
+import torch
+from egnn_pytorch import EGNN_Network
+
+net = EGNN_Network(
+    num_tokens = 21,
+    dim = 32,
+    depth = 3,
+    num_nearest_neighbors = 32,
+    norm_coors = True,              # normalize the relative coordinates
+    coor_weights_clamp_value = 2.   # absolute clamped value for the coordinate weights, needed if you increase the num neareest neighbors
+)
+
+feats = torch.randint(0, 21, (1, 1024)) # (1, 1024)
+coors = torch.randn(1, 1024, 3)         # (1, 1024, 3)
+mask = torch.ones_like(feats).bool()    # (1, 1024)
+
+feats_out, coors_out = net(feats, coors, mask = mask) # (1, 1024, 32), (1, 1024, 3)
+```
+
 ## Examples
 
 To run the protein backbone denoising example, first install `sidechainnet`