diff --git a/src/xlora/xlora.py b/src/xlora/xlora.py
index 527cc6b..5e7fe55 100644
--- a/src/xlora/xlora.py
+++ b/src/xlora/xlora.py
@@ -137,8 +137,8 @@ def hook(module, *args, **kwargs) -> None:
 
             model_peft.internal_xlora_scalings = torch.full(  # type: ignore
                 (payload.batch_size, payload.seq_len, xlora_classifier.n_layers, xlora_classifier.n_classes),
-                payload.override_scaling_pass_value,  # requires_grad=True
-            )  # TODO(EricLBuehler): is the requires_grad=True necessary?
+                payload.override_scaling_pass_value,
+            )
 
             return
 
diff --git a/src/xlora/xlora_classifier.py b/src/xlora/xlora_classifier.py
index 1a174e9..c108ef6 100644
--- a/src/xlora/xlora_classifier.py
+++ b/src/xlora/xlora_classifier.py
@@ -139,14 +139,6 @@ def forward(
         model: PeftModel = self.model  # type: ignore
         with torch.no_grad():
             with model.disable_adapter():
-                # TODO(EricLBuehler): Pending removal following analysis
-                """
-                for module in model.base_model.modules():
-                    if isinstance(module.forward.__self__, xLoRALayer):
-                        inst = module.forward.__self__
-                        inst.disabled = True  # Disable it
-                """
-
                 kwargs["output_hidden_states"] = True
                 kwargs["return_dict"] = True
 
@@ -162,64 +154,11 @@ def forward(
                     **kwargs,
                 )
 
-                # TODO(EricLBuehler): Pending removal following analysis
-                """
-                # Enable the xLoRALayers
-                for module in model.base_model.modules():
-                    if isinstance(module.forward.__self__, xLoRALayer):
-                        inst = module.forward.__self__
-                        inst.disabled = False  # Disable it
-                """
-
         hidden_states = result.hidden_states  # type: ignore
 
         assert hidden_states is not None
         hidden_state = hidden_states[-1]  # Get the last hidden state
 
-        ### Calculate the sequence lengths
-
-        # TODO(all): Pending removal following analysis
-        """
-        # hidden_state=[batch_size, seq_len, hidden_size]
-        if self.config.stop_token_id is None:  # Calculate via attention mask
-            if input_ids is not None:
-                assert attention_mask is not None, (
-                    "Stop token id was not provided, so sequence length calculation via attention mask was attempted"
-                    + "but the attention mask was not given"
-                )
-                sequence_lengths: Union[int, torch.Tensor] = torch.eq(attention_mask, 0).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(hidden_state.device)  # type: ignore
-            else:
-                sequence_lengths = -1
-        else:  # Calculate via stop token id
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.stop_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(hidden_state.device)  # type: ignore
-            else:
-                sequence_lengths = -1
-
-        # AFTER THIS: hidden_state=[batch_size, hidden_size]
-        if self.config.use_mean_pool:
-            assert isinstance(sequence_lengths, torch.Tensor)
-            max_length = hidden_state.shape[1]
-            mask = torch.arange(max_length).expand(len(sequence_lengths), max_length).to(
-                hidden_state.device
-            ) < sequence_lengths.unsqueeze(1)
-
-            # Mask the hidden_states
-            masked_hidden_state = hidden_state * mask.unsqueeze(-1)
-
-            # Sum across the sequence length and divide by actual sequence length
-            summed = torch.sum(masked_hidden_state, dim=1)
-            hidden_state = summed / sequence_lengths.unsqueeze(1)
-        else:
-            # Get it for the last token
-            hidden_state = hidden_state[torch.arange(batch_size, device=hidden_state.device), sequence_lengths]
-        """
-
         ### Classifier run
         # hidden_state=[batch_size, seq_len, hidden_size]
         for layer in self.inner:
diff --git a/src/xlora/xlora_insertion.py b/src/xlora/xlora_insertion.py
index f75859d..40cbd69 100644
--- a/src/xlora/xlora_insertion.py
+++ b/src/xlora/xlora_insertion.py
@@ -20,7 +20,7 @@ class xLoRALayer:
     xLoRA algorithm.
     """
 
-    __slots__ = {"model", "target_forward", "target", "layer_number", "disabled", "config"}
+    __slots__ = {"model", "target_forward", "target", "layer_number", "config"}
 
     def __init__(
         self,
@@ -34,7 +34,6 @@ def __init__(
         self.target_forward = target_forward
         self.target = target
         self.layer_number = layer_number
-        self.disabled = False  # TODO(EricLBuehler): Pending removal following analysis
         self.config = config
 
     @staticmethod