diff --git a/dictionary_learning/buffer.py b/dictionary_learning/buffer.py
index e60a6dd..7d3c260 100644
--- a/dictionary_learning/buffer.py
+++ b/dictionary_learning/buffer.py
@@ -15,6 +15,9 @@ class ActivationBuffer:
     """
     Implements a buffer of activations. The buffer stores activations from a model,
     yields them in batches, and refreshes them when the buffer is less than half full.
+
+    max_activation_norm_multiple: remove all activations with norm greater than median norm * max_activation_norm_multiple. 10 is a good default.
+    This is useful for models like Qwen which have random, unpredictable high norm activation sinks which reduce training effectiveness.
     """
     def __init__(self, 
                  data, # generator which yields text data
@@ -28,8 +31,9 @@ def __init__(self,
                  out_batch_size=8192, # size of batches in which to yield activations
                  device='cpu', # device on which to store the activations
                  remove_bos: bool = False,
-                add_special_tokens: bool = True,
-                 ):
+                 add_special_tokens: bool = True,
+                 max_activation_norm_multiple: int | None = None,
+                ):
         
         if io not in ['in', 'out']:
             raise ValueError("io must be either 'in' or 'out'")
@@ -58,6 +62,7 @@ def __init__(self,
         self.device = device
         self.add_special_tokens = add_special_tokens
         self.remove_bos = remove_bos
+        self.remove_high_norm = max_activation_norm_multiple
 
         if remove_bos and self.model.tokenizer.bos_token_id is None:
             print(
@@ -155,6 +160,13 @@ def refresh(self):
                     first_one = (mask.to(t.int64).cumsum(dim=1) == 1) & mask
                     mask = mask & ~first_one
 
+            if self.remove_high_norm is not None:
+                # some models (like Qwen) have random high norm activation sinks which reduce training effectiveness
+                norms_BL = hidden_states.norm(dim=-1)
+                median_norm = norms_BL.median()
+                norm_mask = norms_BL > median_norm * self.remove_high_norm
+                mask = mask & ~norm_mask
+
             hidden_states = hidden_states[mask]
 
             remaining_space = self.activation_buffer_size - current_idx
diff --git a/dictionary_learning/pytorch_buffer.py b/dictionary_learning/pytorch_buffer.py
index a31d4c6..3533f83 100644
--- a/dictionary_learning/pytorch_buffer.py
+++ b/dictionary_learning/pytorch_buffer.py
@@ -73,6 +73,9 @@ class ActivationBuffer:
     """
     Implements a buffer of activations. The buffer stores activations from a model,
     yields them in batches, and refreshes them when the buffer is less than half full.
+
+    max_activation_norm_multiple: remove all activations with norm greater than median norm * max_activation_norm_multiple. 10 is a good default.
+    This is useful for models like Qwen which have random, unpredictable high norm activation sinks which reduce training effectiveness.
     """
 
     def __init__(
@@ -89,6 +92,7 @@ def __init__(
         device="cpu",  # device on which to store the activations
         remove_bos: bool = False,
         add_special_tokens: bool = True,
+        max_activation_norm_multiple: int | None = None,
     ):
         if io not in ["in", "out"]:
             raise ValueError("io must be either 'in' or 'out'")
@@ -120,6 +124,7 @@ def __init__(
         self.add_special_tokens = add_special_tokens
         self.tokenizer = AutoTokenizer.from_pretrained(model.name_or_path)
         self.remove_bos = remove_bos
+        self.remove_high_norm = max_activation_norm_multiple
 
         if remove_bos and self.tokenizer.bos_token_id is None:
             print(
@@ -208,6 +213,13 @@ def refresh(self):
                     first_one = (mask.to(t.int64).cumsum(dim=1) == 1) & mask
                     mask = mask & ~first_one
 
+            if self.remove_high_norm is not None:
+                # some models (like Qwen) have random high norm activation sinks which reduce training effectiveness
+                norms_BL = hidden_states.norm(dim=-1)
+                median_norm = norms_BL.median()
+                norm_mask = norms_BL > median_norm * self.remove_high_norm
+                mask = mask & ~norm_mask
+
             hidden_states = hidden_states[mask]
 
             remaining_space = self.activation_buffer_size - current_idx