Update app.py

app.py

@@ -599,7 +599,7 @@ class OnnxBgeEmbeddings(Embeddings):
 class LLMEvaluator:
     def __init__(self):
         # Qwen 2.5 0.5B is fast but needs "Few-Shot" examples to be strict.
-        self.repo_id = "onnx-community/Qwen2.5-0.5B-Instruct" 
+        self.repo_id = "onnx-community/Qwen2.5-1.5B-Instruct" 
         self.local_dir = "onnx_qwen_local"
         
         print(f"🔄 Preparing CPU LLM: {self.repo_id}...")
@@ -637,8 +637,8 @@ class LLMEvaluator:
         Your job is to check if the Student Answer is FACTUALLY present in the Context.
 
         GRADING ALGORITHM:
-        1. IF the Student Answer mentions things NOT in the Context -> PENALTY (-100%).
-        2. IF the Student Answer interprets the text opposite to its meaning -> PENALTY (-100%).
+        1. IF the Student Answer mentions things NOT in the Context -> PENALTY (-50% of the marks).
+        2. IF the Student Answer interprets the text opposite to its meaning -> PENALTY (-100% of the marks).
         3. IF the Student Answer is generic fluff -> SCORE: 0.
 
         --- EXAMPLE 1 (HALLUCINATION) ---