update

tests/test_hf_model.py

@@ -160,6 +160,7 @@ class TestMath:
         assert_model(response)
         assert '2' in response
 
+
 class TestReward:
     """Test cases for base model."""
 
@@ -181,46 +182,57 @@ class TestReward:
         tokenizer = AutoTokenizer.from_pretrained(model_name,
                                                   trust_remote_code=True,
                                                   use_fast=usefast)
-        model = AutoModel.from_pretrained(model_name, device_map="cuda", 
-                                          torch_dtype=torch.float16, 
-                                          trust_remote_code=True,)
-        tokenizer = AutoTokenizer.from_pretrained(model_name, 
+        model = AutoModel.from_pretrained(
+            model_name,
+            device_map='cuda',
+            torch_dtype=torch.float16,
+            trust_remote_code=True,
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_name,
                                                   trust_remote_code=True)
 
-        chat_1 = [
-            {"role": "user", "content": "Hello! What's your name?"},
-            {"role": "assistant", "content": "My name is InternLM2! A helpful AI assistant. What can I do for you?"}
-        ]
-        chat_2 = [
-            {"role": "user", "content": "Hello! What's your name?"}, 
-            {"role": "assistant", "content": "I have no idea."}
-        ]
+        chat_1 = [{
+            'role': 'user',
+            'content': "Hello! What's your name?"
+        }, {
+            'role':
+            'assistant',
+            'content':
+            'I am InternLM2! A helpful AI assistant. What can I do for you?'
+        }]
+        chat_2 = [{
+            'role': 'user',
+            'content': "Hello! What's your name?"
+        }, {
+            'role': 'assistant',
+            'content': 'I have no idea.'
+        }]
 
         # get reward score for a single chat
         score1 = model.get_score(tokenizer, chat_1)
         score2 = model.get_score(tokenizer, chat_2)
-        print("score1: ", score1)
-        print("score2: ", score2)
-        assert score1 > 0.5 && score1 < 1 && score2 < 0
-        
+        print('score1: ', score1)
+        print('score2: ', score2)
+        assert score1 > 0.5 & score1 < 1 & score2 < 0
+
         # batch inference, get multiple scores at once
         scores = model.get_scores(tokenizer, [chat_1, chat_2])
-        print("scores: ", scores)
-        assert scores[0] > 0.5 && scores[0] < 1 && scores[1] < 0        
-        
+        print('scores: ', scores)
+        assert scores[0] > 0.5 & scores[0] < 1 & scores[1] < 0
+
         # compare whether chat_1 is better than chat_2
         compare_res = model.compare(tokenizer, chat_1, chat_2)
-        print("compare_res: ", compare_res)
+        print('compare_res: ', compare_res)
         assert compare_res
         # >>> compare_res:  True
-        
+
         # rank multiple chats, it will return the ranking index of each chat
-        # the chat with the highest score will have ranking index as 0 
+        # the chat with the highest score will have ranking index as 0
         rank_res = model.rank(tokenizer, [chat_1, chat_2])
-        print("rank_res: ", rank_res)  # lower index means higher score
-        # >>> rank_res:  [0, 1]  
-        assert rank_res[0] == 0 && rank_res[1] == 1
-)
+        print('rank_res: ', rank_res)  # lower index means higher score
+        # >>> rank_res:  [0, 1]
+        assert rank_res[0] == 0 & rank_res[1] == 1
+
 
 class TestMMModel:
     """Test cases for base model."""