diff --git a/llmfoundry/callbacks/kill_loss_spike_callback.py b/llmfoundry/callbacks/kill_loss_spike_callback.py
index c0c27320ce..6403ac76c2 100644
--- a/llmfoundry/callbacks/kill_loss_spike_callback.py
+++ b/llmfoundry/callbacks/kill_loss_spike_callback.py
@@ -40,9 +40,9 @@ class KillLossSpike(Callback):
         outlier_multiplier (int): The multiplier used to determine if a loss is an outlier. A loss is considered an
                                   outlier if it is outlier_multiplier times greater than the mean of losses in
                                   the current window. Default is 2.
-        window_size (int): The size of the rolling window used to track recent losses. This is set to 1/20 of the total training batches by default, with a minimum of 100 steps.
+        window_size (int): The size of the rolling window used to track recent losses. This is set to 1/20 of the total training batches, with a minimum of 100 steps.
         loss_cap (int): The maximum allowable loss. If the training loss consistently exceeds this value,
-                        it is considered a diverging or unstable run. This is set to the maximum loss from the first window of losses by default.
+                        it is considered a diverging or unstable run. This is set to the maximum loss from the first window of losses.
 
     Raises:
         LossSpikeError: If log_only is False and a loss spike or persistently high loss is detected, this error is
@@ -54,17 +54,12 @@ def __init__(
         log_only: bool = True,
         patience: int = 4,
         outlier_multiplier: float = 2,
-        window_size: int = None,
-        loss_cap: float = None,
     ):
-        self._enabled = (dist.get_global_rank() == 0)
+        # self._enabled = (dist.get_global_rank() == 0)
         self.log_only = log_only
         self.patience = patience
         self.outlier_multiplier = outlier_multiplier
-        self.window_size = window_size
-        self.loss_cap = loss_cap
         self.outlier_counter = 0
-        self.loss_window = deque(maxlen=self.window_size)
 
     def detect_loss_spike(self, train_loss: float, running_loss_avg: float):
         # Train loss is an outlier
@@ -97,23 +92,19 @@ def detect_high_losses(self, current_step: int):
         return False
 
     def fit_start(self, state: State, logger: Logger) -> None:
-        #Set the window to a fraction of the total number of training batches, minimum 100.
-        if not self.window_size:
-            if state.max_duration.unit == TimeUnit.EPOCH:
-                self.window_size = max(
-                    MIN_WINDOW_SIZE,
-                    round(state.dataloader_len * state.max_duration.value / 20),
-                )
-            elif state.max_duration.unit == TimeUnit.BATCH:
-                self.window_size = max(
-                    MIN_WINDOW_SIZE,
-                    round(state.max_duration.value / 20),
-                )
-            elif state.max_duration.unit == TimeUnit.TOKEN:
-                self.window_size = max(
-                    MIN_WINDOW_SIZE,
-                    round(state.max_duration.value / 20),
-                )
+        # Set the window size to a fraction of the total number of training batches for the run, minimum 100 batches.
+        if state.max_duration.unit == TimeUnit.EPOCH:
+            self.window_size = max(
+                MIN_WINDOW_SIZE,
+                round(state.dataloader_len * state.max_duration.value / 20),
+            )
+        elif state.max_duration.unit == TimeUnit.BATCH or state.max_duration.unit == TimeUnit.TOKEN:
+            self.window_size = max(
+                MIN_WINDOW_SIZE,
+                round(state.max_duration.value / 20),
+            )
+        self.loss_window = deque(maxlen=self.window_size)    
+            
 
     def batch_end(self, state: State, logger: Logger) -> None: