Fix LLM for ai assisstant

models/llm.py

@@ -13,16 +13,11 @@ from config import LLM_MODEL, DEVICE, MODELS_DIR, MAX_NEW_TOKENS, TEMPERATURE, T
 
 class LanguageModel:
     def __init__(self):
-<<<<<<< HEAD
-        print(f"Loading language model: {LLM_MODEL}")
+        print(f"Loading local language model: {LLM_MODEL} on {DEVICE}...")
         
-        # Quantization config for GPU (optional, only if you want smaller models)
         quantization_config = None
-        
-        # Only use quantization if on GPU
         if DEVICE == "cuda":
             try:
-                # Try 8-bit quantization (recommended)
                 quantization_config = BitsAndBytesConfig(
                     load_in_8bit=True,
                     llm_int8_threshold=6.0
@@ -32,34 +27,17 @@ class LanguageModel:
                 print("8-bit quantization not available, using full precision")
         
         # Load tokenizer
-=======
-        print(f"Loading local language model: {LLM_MODEL} on {DEVICE}...")
-        
-        quantization_config = None
-        if DEVICE == "cuda":
-            try:
-                quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_threshold=6.0)
-            except:
-                pass
-        
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1
         self.tokenizer = AutoTokenizer.from_pretrained(
             LLM_MODEL,
             cache_dir=str(MODELS_DIR),
             trust_remote_code=True
         )
         
-<<<<<<< HEAD
         # Set pad token if not set
         if self.tokenizer.pad_token is None:
             self.tokenizer.pad_token = self.tokenizer.eos_token
         
         # Load model
-=======
-        if self.tokenizer.pad_token is None:
-            self.tokenizer.pad_token = self.tokenizer.eos_token
-        
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1
         self.model = AutoModelForCausalLM.from_pretrained(
             LLM_MODEL,
             cache_dir=str(MODELS_DIR),
@@ -73,16 +51,11 @@ class LanguageModel:
             self.model = self.model.to(DEVICE)
         
         self.model.eval()
-<<<<<<< HEAD
-        print(f"✓ Language model loaded on {DEVICE}")
-=======
-        print(f"✓ Local Language model loaded successfully!")
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1
+        print(f"✓ Local Language model loaded successfully on {DEVICE}!")
     
     def generate(
         self,
         prompt: str,
-<<<<<<< HEAD
         max_new_tokens: int = 150,
         temperature: float = TEMPERATURE,
         top_p: float = TOP_P
@@ -97,31 +70,10 @@ class LanguageModel:
             max_length=768,  # cap input; single-MCQ prompts are <512 tokens
         ).to(self.model.device)
 
-=======
-        max_new_tokens: int = MAX_NEW_TOKENS,
-        temperature: float = TEMPERATURE,
-        top_p: float = TOP_P
-    ) -> str:
-        """Generate text locally"""
-        
-        # 👉 THE FIX: TinyLlama requires strict Chat Templates. 
-        # Without this, it loops infinitely and causes timeouts.
-        is_chat = "chat" in LLM_MODEL.lower() or "instruct" in LLM_MODEL.lower()
-        if is_chat:
-            formatted_prompt = f"<|system|>\nYou are an expert educational AI that strictly follows instructions.</s>\n<|user|>\n{prompt}</s>\n<|assistant|>\n"
-        else:
-            formatted_prompt = prompt
-
-        inputs = self.tokenizer(formatted_prompt, return_tensors="pt").to(self.model.device)
-        
-        print(f"🤖 Generating locally (up to {max_new_tokens} tokens). This may take 2-4 minutes on a free CPU Space...")
-        
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1
         with torch.no_grad():
             outputs = self.model.generate(
                 **inputs,
                 max_new_tokens=max_new_tokens,
-<<<<<<< HEAD
                 max_length=None,
                 do_sample=False,          # greedy — ~3x faster than sampling
                 pad_token_id=self.tokenizer.pad_token_id,
@@ -134,40 +86,13 @@ class LanguageModel:
         generated_ids = outputs[0][input_len:]
         generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
         return generated_text.strip()
-=======
-                temperature=temperature,
-                top_p=top_p,
-                do_sample=True,
-                pad_token_id=self.tokenizer.pad_token_id,
-                eos_token_id=self.tokenizer.eos_token_id
-            )
-        
-        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        # Clean up the output to return ONLY the generated MCQs
-        clean_input = self.tokenizer.decode(inputs['input_ids'][0], skip_special_tokens=True)
-        if generated_text.startswith(clean_input):
-            generated_text = generated_text[len(clean_input):].strip()
-            
-        if "<|assistant|>" in generated_text:
-            generated_text = generated_text.split("<|assistant|>")[-1].strip()
-            
-        return generated_text
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1
 
 # Singleton instance
 _llm_model = None
 
 def get_llm_model() -> LanguageModel:
-<<<<<<< HEAD
     """Get or create LLM instance"""
     global _llm_model
     if _llm_model is None:
         _llm_model = LanguageModel()
     return _llm_model
-=======
-    global _llm_model
-    if _llm_model is None:
-        _llm_model = LanguageModel()
-    return _llm_model
->>>>>>> a06ff7e70b83069b439c95563bab4f3822d242b1