fix glm4v-merge_lora (modelscope#1104)

swift/llm/utils/argument.py

@@ -1,4 +1,5 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import datetime as dt
 import inspect
 import math
 import os
@@ -1260,7 +1261,7 @@ def __post_init__(self):
 @dataclass
 class EvalArguments(InferArguments):
 
-    name: Optional[str] = None
+    name: Optional[str] = field(default_factory=lambda: dt.datetime.now().strftime('%Y%m%d-%H%M%S'))
 
     eval_url: Optional[str] = None
 

swift/llm/utils/client_utils.py

@@ -160,6 +160,8 @@ def inference_client(
 
     if is_chat_request is None:
         is_chat_request = _is_chat
+    assert is_chat_request is not None, (
+        'Please set the `is_chat_request` parameter to indicate whether the model is a chat model.')
     data = {k: v for k, v in request_config.__dict__.items() if not k.startswith('__')}
     if is_chat_request:
         messages = history_to_messages(history, query, system)

swift/llm/utils/model.py

@@ -1485,6 +1485,7 @@ def get_model_tokenizer_chatglm(model_dir: str,
         remove_property(tokenizer_cls, tokenizer_config)
         kwargs['tokenizer'] = tokenizer_cls.from_pretrained(model_dir, trust_remote_code=True)
     model, tokenizer = get_model_tokenizer_from_repo(model_dir, torch_dtype, model_kwargs, load_model, **kwargs)
+    tokenizer.init_kwargs['image_size'] = 1120
     if model is not None:
         from torch.nn import CrossEntropyLoss
         __old_forward = CrossEntropyLoss.forward

swift/llm/utils/preprocess.py

@@ -145,12 +145,12 @@ def __call__(self, dataset: HfDataset) -> HfDataset:
         kwargs = {}
         if has_system:
             kwargs['system'] = system
-        if has_history:
-            kwargs['history'] = history
         kwargs.update({
             'query': query,
             'response': response,
         })
+        if has_history:
+            kwargs['history'] = history
         dataset = HfDataset.from_dict(kwargs)
         return dataset
 

swift/llm/utils/protocol.py

@@ -12,7 +12,7 @@ def random_uuid() -> str:
 @dataclass
 class Model:
     id: str  # model_type
-    is_chat: bool  # chat model or generation model
+    is_chat: Optional[bool] = None  # chat model or generation model
     is_multimodal: bool = False
 
     object: str = 'model'

tests/llm/test_run2.py

@@ -105,7 +105,9 @@ def test_glm4v_9b_chat(self):
                 lazy_tokenize=False))
         best_model_checkpoint = output['best_model_checkpoint']
         torch.cuda.empty_cache()
-        infer_main(InferArguments(ckpt_dir=best_model_checkpoint, load_dataset_config=True, val_dataset_sample=2))
+        infer_main(
+            InferArguments(
+                ckpt_dir=best_model_checkpoint, load_dataset_config=True, val_dataset_sample=2, merge_lora=True))
 
     def test_baichuan2_chat_int4(self):
         if not __name__ == '__main__':