load model

ice_breaking_challenge/__init__.py

@@ -2,10 +2,13 @@ import os
 
 from flask import Flask, session
 from flask_session import Session
+from ice_breaking_challenge.models.model_loader import load_model_with_lora
 
+model = None
 
 def create_app(test_config=None):
     """Create and configure an instance of the Flask application."""
+    global model
     app = Flask(__name__, instance_relative_config=True)
     app.config.from_mapping(
         # a default secret that should be overridden by instance config
@@ -16,6 +19,9 @@ def create_app(test_config=None):
     app.config['SESSION_TYPE'] = 'filesystem'
     Session(app)
 
+    # model loading
+    model = load_model_with_lora()
+    
     if test_config is None:
         # load the instance config, if it exists, when not testing
         app.config.from_pyfile("config.py", silent=True)

ice_breaking_challenge/models/model_loader.py

@@ -0,0 +1,21 @@
+import keras_nlp
+
+MODEL_NAME = "gemma2_instruct_2b_en"
+LORA_WEIGHT_PATH = "weights/gemma2_it_2b_icebreaking.lora.h5"
+
+def load_model_with_lora(model_name:str = MODEL_NAME, lora_weight_path: str = LORA_WEIGHT_PATH):
+    """
+    Keras 기반 모델 로드 및 LoRA 가중치 적용
+
+    Args:
+        model_name (str): 로드할 모델의 이름
+        lora_weight_path (str): 적용할 LoRA 가중치 파일의 경로
+
+    Returns:
+        keras_nlp.models.GemmaCausalLM: 로드된 모델
+    """
+    model = keras_nlp.models.GemmaCausalLM.from_preset(model_name)
+
+    model.backbone.load_lora_weights(lora_weight_path)
+
+    return model