new

steer_vector.py

@@ -459,18 +459,6 @@ def main():
     else:
         assert "Not supported dataset name!"
         
-        
-    target_directory = f"save_for_eval/{args.dataset_name}/{args.model_name}_jailbreak"
-    benign_data, adverse_data = load_npy_shapes(target_directory, args.steer_place)
-    y_zero=np.zeros(len(benign_data))
-    y_one=np.ones(len(adverse_data))
-    
-    data_embedding = np.concatenate((benign_data, adverse_data), axis=0)
-    gts = np.concatenate((y_zero, y_one), axis=0)
-    
-    train_index, val_index, test_index=split_indices(len(data_embedding), args.train_ratio, args.val_ratio)
-    
-    
 
 
     # ============================================================
@@ -487,12 +475,42 @@ def main():
             device_map="auto",
             token = ''
         )
+        tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, token = '')
+        
+        prompts = []
+        qa_pairs = []
+        categories = []
+        
+        length = len(dataset)
+        
+        for i in tqdm(range(length)):
+            question = dataset[i]['query']
+            adversary = dataset[i]['target']
+            prompt = tokenizer(
+                    f"Answer the question concisely. Q: {question}" + " A:" + adversary,
+                    return_tensors='pt'
+                ).input_ids.cuda()
+                   
+            prompts.append(prompt)
+            qa_pairs.append({'Question': question, 'Answer': adversary})
+            categories.append(1)  # 1 = adverse
+        
+        for i in tqdm(range(length)):
+            question = dataset[i]['query']
+            clean= dataset[i]['response']
+            prompt = tokenizer(
+                    f"Answer the question concisely. Q: {question}" + " A:" + clean,
+                    return_tensors='pt'
+                ).input_ids.cuda()
+                   
+            prompts.append(prompt)
+            qa_pairs.append({'Question': question, 'Answer': clean})
+            categories.append(0)  # 0 = benign
+            
+        train_index, val_index, test_index=split_indices(len(prompts), args.train_ratio, args.val_ratio)
+        
         
-        train_data = [data_embedding[i] for i in train_index]
-        train_labels = [gts[i] for i in train_index]
         
-        test_data = [data_embedding[i] for i in test_index]
-        test_labels = [gts[i] for i in test_index]
         
         # ====== 6.4 冻结 LLM，只训练 SV 参数 ======
         num_layers = model.config.num_hidden_layers