.

app.py

@@ -20,14 +20,13 @@ except ImportError:
         return decorator
     spaces = type('spaces', (), {'GPU': spaces_gpu_decorator})
 
-# Model configuration - can be replaced with other models
+# Model configuration - Foundation-Sec-8B only
 MODEL_NAME = "fdtn-ai/Foundation-Sec-8B"
-#MODEL_NAME = "sshleifer/tiny-gpt2"
 
 # Initialize tokenizer and model using pipeline approach
 print(f"Loading model: {MODEL_NAME}")
 try:
-    print(f"Initializing text generation model: {MODEL_NAME}")
+    print(f"Initializing Foundation-Sec-8B model...")
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     text_pipeline = pipeline(
         "text-generation",
@@ -37,14 +36,14 @@ try:
         device_map="auto",
         trust_remote_code=True
     )
-    print(f"Model initialized successfully: {MODEL_NAME}")
+    print(f"Foundation-Sec-8B model initialized successfully")
     
     # Extract model and tokenizer from pipeline for direct access
     model = text_pipeline.model
     tok = text_pipeline.tokenizer
     
 except Exception as e:
-    print(f"Error initializing model {MODEL_NAME}: {str(e)}")
+    print(f"Error initializing Foundation-Sec-8B model: {str(e)}")
     print("Trying with simplified parameters...")
     
     try:
@@ -56,36 +55,11 @@ except Exception as e:
         )
         model = text_pipeline.model
         tok = text_pipeline.tokenizer
-        print(f"Model loaded with simplified parameters: {MODEL_NAME}")
+        print(f"Foundation-Sec-8B model loaded with simplified parameters")
         
     except Exception as e2:
-        print(f"Second attempt failed: {str(e2)}")
-        print("Falling back to distilgpt2 (uses safetensors)...")
-        
-        # Use distilgpt2 which uses safetensors format and is more compatible
-        MODEL_NAME = "distilgpt2"
-        try:
-            tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-            text_pipeline = pipeline(
-                "text-generation",
-                model=MODEL_NAME,
-                tokenizer=tokenizer
-            )
-            model = text_pipeline.model
-            tok = text_pipeline.tokenizer
-            print(f"Fallback model loaded: {MODEL_NAME}")
-            
-        except Exception as e3:
-            print(f"Fallback also failed: {str(e3)}")
-            print("Trying direct model loading as last resort...")
-            
-            # Last resort: direct loading without pipeline
-            try:
-                tok = AutoTokenizer.from_pretrained(MODEL_NAME)
-                model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).eval()
-                print(f"Direct loading successful: {MODEL_NAME}")
-            except Exception as e4:
-                raise RuntimeError(f"All model loading attempts failed. Last error: {str(e4)}")
+        print(f"Failed to load Foundation-Sec-8B model: {str(e2)}")
+        raise RuntimeError(f"Could not load Foundation-Sec-8B model. Please ensure the model is accessible and try again. Error: {str(e2)}")
 
 # Log device information
 if hasattr(model, 'device'):