Update Model.py

Model.py

@@ -68,7 +68,7 @@ unk_id = sp.piece_to_id("<unk>")
 vocab_size = sp.get_piece_size()
 print(f"✅ Vocabulary size: {vocab_size}")
 
-max_len = 230
+max_len = 150
 batch_size = 128
 
 def text_to_ids(text):
@@ -251,7 +251,7 @@ class LoU(layers.Layer):
         # cast back to original dtype for downstream layers
         return tf.cast(out, x.dtype)
 
-class ReLaM(tf.keras.Model):
+class ReLM(tf.keras.Model):
     def __init__(self, vocab_size, max_seq_len, d_model, n_layers, dropout_rate=0.1):
         super().__init__()
         self.token_embedding = layers.Embedding(vocab_size, d_model)
@@ -298,7 +298,7 @@ def create_lr_schedule(initial_lr=5e-5, decay_steps=10000, decay_rate=0.9):
     )
 
 # 모델 생성
-model = ReLaM(
+model = ReLM(
     vocab_size=vocab_size,
     max_seq_len=max_len,
     d_model=128,
@@ -340,7 +340,7 @@ history = model.fit(
 model.save_weights("Cobra.weights.h5")
 print("모델 가중치 저장 완료!")
 
-def generate_text_topp(model, prompt, max_len=512, max_gen=512, p=0.9, temperature=0.8, min_len=20):
+def generate_text_topp(model, prompt, max_len=150, max_gen=150, p=0.9, temperature=0.8, min_len=20):
     model_input = text_to_ids(f"<start> {prompt}")
     model_input = model_input[:max_len]
     generated = list(model_input)
@@ -370,4 +370,4 @@ def generate_text_topp(model, prompt, max_len=512, max_gen=512, p=0.9, temperatu
     return ids_to_text(generated)
 
 print("\n\n===== 생성 결과 =====")  
-print(generate_text_topp(model, "", p=0.9))
+print(generate_text_topp(model, "지난 2년 동안 출연연이 국가가 필요한 연구를", p=0.9))