diff --git a/app.py b/app.py
index 2f2a898..c158e0a 100644
--- a/app.py
+++ b/app.py
@@ -16,7 +16,7 @@ from tavily import TavilyClient
 from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizer
 
 # Import from config
-from config import GENERATOR_MODEL_DIR, logger
+from config import GENERATOR_MODEL_DIR, GENERATOR_MODEL_REPO_ID, logger
 from src import (
     apply_chat_template,
     build_user_prompt,
@@ -64,13 +64,28 @@ def setup_model_and_tokenizer(model_path: str):
     """Initialize model and tokenizer."""
     logger.info(f"Setting up model from {model_path}...")
 
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path,
-        torch_dtype="float16",
-        device_map="auto",
-        trust_remote_code=True,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    try:
+        # Try loading from the provided path first
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            torch_dtype="float16",
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    except Exception as e:
+        logger.warning(f"Failed to load from local path: {e}")
+        logger.info(f"Attempting to load directly from Hugging Face: {GENERATOR_MODEL_REPO_ID}")
+
+        # Fallback to the Hugging Face model repository
+        model = AutoModelForCausalLM.from_pretrained(
+            GENERATOR_MODEL_REPO_ID,
+            torch_dtype="float16",
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        tokenizer = AutoTokenizer.from_pretrained(GENERATOR_MODEL_REPO_ID, trust_remote_code=True)
+        logger.info(f"Successfully loaded model from Hugging Face repo")
 
     # Defaulting to the one used in inference.py, adjust if needed for your specific model
     assistant_marker = "<|start_header_id|>assistant<|end_header_id|>"
@@ -897,9 +912,7 @@ def main():
         # Display error if model fails to load
         with gr.Blocks() as demo:
             gr.Markdown("# Critical Error")
-            gr.Markdown(
-                f"Failed to load model or tokenizer from '{model_path}'. Check the path and ensure the model exists.\n\nError: {e}"
-            )
+            gr.Markdown(f"Failed to load both local model and Hugging Face model. Error: {e}")
             demo.launch(share=True)
         sys.exit(1)  # Exit if model loading fails