[fix] fix freeze top N layers

model/reward/instructor/trainer.py

@@ -98,6 +98,9 @@ if __name__ == "__main__":
     if 'freeze_layer' in training_conf:
         num_layer = training_conf['freeze_layer']
         model = freeze_top_n_layers(model, num_layer)
+        model_parameters = filter(lambda p: p.requires_grad, model.parameters())
+        params = sum([np.prod(p.size()) for p in model_parameters])
+        print('Number of trainable : {}M'.format(int(params/1e6)))
 
     tokenizer = get_tokenizer(model_name)
     args = CustomTrainingArguments(