fix some bugs

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Bug: Refactoring Breaks Dataset Selection and Training Configuration

The refactoring of weclone/utils/config.py removed critical, dynamic logic for train_sft and make_dataset argument types. This includes the absence of:

• Conditional dataset selection: The system no longer falls back to "wechat-sft-with-history" when prompt_with_history is enabled but the dataset lacks history columns. It also fails to conditionally select "wechat-img-rec-sft" (with vision_api enabled) or "wechat-mllm-sft" (otherwise) when image modality is included. The removal of include_type from WCTrainSftConfig further compounds this.
• Automatic training configuration: The do_train=True and output_dir settings are no longer automatically applied for training configurations.

This omission leads to incorrect dataset usage and improper training setups, causing potential failures or incorrect behavior.

weclone/utils/config.py#L49-L78

WeClone/weclone/utils/config.py

Lines 49 to 78 in 45a5797

	def create_config_by_arg_type(arg_type: str, wc_config: WcConfig) -> BaseModel:
	"""根据参数类型创建对应的配置对象,添加可能用到的参数,添加的参数会在model_validator中删除"""
	if arg_type == "cli_args":
	return wc_config.cli_args
	common_config = wc_config.common_args.model_dump()
	if arg_type == "web_demo" or arg_type == "api_service":
	config_dict = {**common_config, **wc_config.infer_args.model_dump()}
	return WCInferConfig(**config_dict)
	elif arg_type == "train_sft":
	config_dict = {**common_config, **wc_config.train_sft_args.model_dump()}
	return WCTrainSftConfig(**config_dict)
	elif arg_type == "make_dataset":
	make_dataset_config = wc_config.make_dataset_args.model_dump()
	# ToDo 下面三个参数放到common里？
	train_sft_args = wc_config.train_sft_args
	extra_values = {
	"dataset": train_sft_args.dataset,
	"dataset_dir": train_sft_args.dataset_dir,
	"cutoff_len": train_sft_args.cutoff_len,
	}
	config_dict = {**common_config, **make_dataset_config, **extra_values}
	return WCMakeDatasetConfig(**config_dict)
	else:
	raise ValueError("暂不支持的参数类型")

Fix in Cursor

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