diff --git a/codes/models/gpt_voice/gpt_asr_hf.py b/codes/models/gpt_voice/gpt_asr_hf.py
index 4439a1d4..67e509f4 100644
--- a/codes/models/gpt_voice/gpt_asr_hf.py
+++ b/codes/models/gpt_voice/gpt_asr_hf.py
@@ -231,7 +231,7 @@ class GptAsrHf(nn.Module):
         self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
 
 
-    def get_logits(self, mel_inputs, text_targets, get_attns):
+    def get_logits(self, mel_inputs, text_targets, get_attns=False):
         # Pad front and back. Pad at front is the "START" token.
         text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)
         text_targets = F.pad(text_targets, (0, self.max_symbols_per_phrase - text_targets.shape[1]))
@@ -290,25 +290,29 @@ def register_gpt_asr_hf(opt_net, opt):
 
 # Quick script that loads a model and halves the number of layers, then saves that model.
 def distill():
-    gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=768, heads=12)
-    gpt.load_state_dict(torch.load('../experiments/train_gpt_asr_mass/models/21500_mel_gen.pth'))
+    gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=512, heads=8)
+    gpt.load_state_dict(torch.load('X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_ema.pth'))
     rc = 0
     i = 0
-    while i < len(gpt.gpt.layers.layers):
+    while i < len(gpt.gpt.h):
         if rc % 2 != 0:
-            del gpt.gpt.layers.layers[i]
+            del gpt.gpt.h[i]
         else:
             i += 1
         rc += 1
-    torch.save(gpt.state_dict(), '../experiments/train_gpt_asr_mass/models/21500_mel_gen_distilled.pth')
+    torch.save(gpt.state_dict(), 'X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_distilled.pth')
 
 
 if __name__ == '__main__':
+    distill()
+
+    '''
     gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=16, model_dim=512, heads=8)
     #l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100)))
     start = time()
     gpt.inference(torch.randn(1,80,350), num_beams=1)
     print(f"Elapsed: {time()-start}")
+    '''
 
     '''
     with torch.no_grad():