Update modeling_quiet.py

modeling_quiet.py

@@ -1254,27 +1254,27 @@ class QuietForCausalLM(QuietPreTrainedModel, GenerationMixin):
         self.eval_mode = False
         
         num_talk = 1
-		talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
-		if self.use_weighted_talk_head:
-			talk_output_dim = 1
-		else:
-			talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
-
-		if not self.merged_lm_and_talk_heads:
-			if self.use_complex_talk_head:
-				self.talk_head = nn.ModuleList([nn.Sequential(
-					nn.Linear(talk_input_dim, config.hidden_size),
-					nn.ReLU(),
-					nn.Linear(config.hidden_size, config.hidden_size),
-					nn.ReLU(),
-					nn.Linear(config.hidden_size, talk_output_dim, bias=False)
-				)])
-			else:
-				self.talk_head = nn.ModuleList([nn.Sequential(
-					nn.Linear(talk_input_dim, talk_output_dim, bias=False)
-				)])
-			
-			self.mixing_head = nn.Linear(config.hidden_size * 2, 1)
+        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
+        if self.use_weighted_talk_head:
+            talk_output_dim = 1
+        else:
+            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
+
+        if not self.merged_lm_and_talk_heads:
+            if self.use_complex_talk_head:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
+                )])
+            else:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, talk_output_dim, bias=False)
+                )])
+            
+            self.mixing_head = nn.Linear(config.hidden_size * 2, 1)
                 
         self.apply(self._init_weights)