Load checkpoint from Trainer
class MyModel(nn.Module):
    def __init__(self, model_args, data_args, training_args, lora_config): 
        super().__init__()
        self.model_args = model_args
        self.data_args = data_args
        self.training_args = training_args
        self.device = training_args.device
        self.llama = LlamaForCausalLM.from_pretrained(\
            model_args.model_name_or_path, \
            cache_dir = model_args.cache_dir, \
        )
        self.llama= get_peft_model(self.llama, lora_config)This is my model code, I customized it.model = MyModel(model_args, data_args, training_args, lora_config)

trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_data,
        eval_dataset=valid_data,
        tokenizer = tokenizer,
        data_collator = data_collator,
        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
    )During the train, I saved a model every 1000 steps.And I’d like to evaluate this 1000 steps after training by loading checkpoint.In this case,model = AutoModel.from_pretrained(“checkpoint_dir”)  causes an error and says that there is no config.json.In the example code at Huggingface transformers, to begin with, the model is defined Huggingface model like GPT2LMHeadModel, which allows model = GPT2LMHeadModel.from_pretrained(“checkpoint_dir”) to work.This is a different situation from mine(custom model)How can I load the saved checkpoint model which was defined as a custom model, with Huggingface Trainer to evaluate this model?