Update tinyllama_inference.py to use deepseek-ai/deepseek-coder-1.3b-instruct

tinyllama_inference.py

@@ -15,12 +15,13 @@ def load_model():
     return tokenizer, model
 
 def evaluate_code(question, code):
-    # Refined prompt: clearly instructs the model to output exactly one JSON object.
+    # Refined prompt: instruct the model to output exactly one JSON object.
     prompt = f"""You are an expert code evaluator.
-Evaluate the following solution and provide your evaluation as a valid JSON object.
-The JSON object must have exactly two keys:
+Evaluate the following solution for the given problem.
+Respond with exactly one JSON object (with no extra text) that has exactly two keys:
   "stars": an integer between 0 and 5 (0 means completely incorrect, 5 means excellent),
-  "feedback": a concise string message explaining your evaluation.
+  "feedback": a concise string message.
+The JSON must start with '{{' and end with '}}'.
 Do not output any text besides the JSON.
 Question: "{question}"
 Solution: "{code}"
@@ -30,17 +31,17 @@ Your response:"""
     outputs = model.generate(
         **inputs,
         max_new_tokens=100,      # Allow enough tokens for a complete response
-        temperature=0.2,         # Small randomness for creativity but mostly deterministic
+        temperature=0.2,         # Small randomness for a bit of creativity
         pad_token_id=tokenizer.eos_token_id,
         do_sample=True            # Enable sampling to encourage model generation
     )
     response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    print("Raw model response:", response_text)  # Debug: view raw output
+    print("Raw model response:", response_text)  # Debug output
     
-    # Use non-greedy regex to extract the JSON object
-    match = re.search(r'\{.*?\}', response_text)
-    if match:
-        json_text = match.group(0)
+    # Use findall to get all JSON objects and take the last one
+    matches = re.findall(r'\{.*?\}', response_text)
+    if matches:
+        json_text = matches[-1]
         try:
             result = json.loads(json_text)
         except Exception as e: