Error while using load_best_model_at_end  with LoRA adapters inside Trainer and SFTTrainer
Author: deshwalmahesh
Pull request: no

### System Info

I'm using version `4.37` and when you use any of the model like `AutoModelForCausalLM,  AutoModelForSequenceClassification` along with LoRA adapters, you get error after training gets finished. I used `load_best_model_at_end`  with `TrainingArguments` on both the above mentioned models with `Trainer` as well as `SFTTrainer`. I tried it with both Classification and SFT


### Who can help?

@muellerz @pacman100 @ArthurZucker @younes

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

1. Model:

```
model = PhiForSequenceClassification.from_pretrained(model_name, num_labels = 1, trust_remote_code=True, torch_dtype=torch.bfloat16)

 model.config.pad_token_id = model.config.eos_token_id

model.gradient_checkpointing_enable() #gradient checkpointing to save memory

 model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True) 

lora_config = LoraConfig(
            r=256,
            lora_alpha=512,
            target_modules=[
            'q_proj','k_proj','v_proj','dense','fc1','fc2',], #print(model) will show the modules to use
            bias="none",
            lora_dropout=0.05,
            task_type=TaskType.SEQ_CLS)


 model = get_peft_model(model, lora_config) # LORA

```

3. Args: 
 ```
training_args = TrainingArguments(load_best_model_at_end = False )

trainer = Trainer(
        model = model,
        train_dataset=tokenized_train_dataset,
        eval_dataset=tokenized_val_dataset,
        args=training_args,
        data_collator = DataCollatorWithPadding(tokenizer=tokenizer),
        compute_metrics = compute_metrics_for_regression,
        callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]
    )

trainer.train()
```

### Expected behavior

it should not give the below at the end of training:


```
RuntimeError: Error(s) in loading state_dict for PeftModelForSequenceClassification:
        Missing key(s) in state_dict: "base_model.model.model.embed_tokens.weight", "base_model.model.model.layers.0.self_attn.q_proj.base_layer.weight", "base_model.model.model.layers.0.self_attn.q_proj.base_layer.bias", "base_model.model.model.layers.0.self_attn.k_proj.base_layer.weight", "base_model.model.model.layers.0.self_attn.k_proj.base_layer.bias", "base_model.model.model.layers.0.self_attn.v_proj.base_layer.weight", "base_model.model.model.layers.0.self_attn.v_proj.base_layer.bias", "base_model.model.model.layers.0.self_attn.dense.base_layer.weight", "base_model.model.model.layers.0.self_attn.dense.base_layer.bias", "base_model.model.model.layers.0.mlp.fc1.base_layer.weight", "base_model.model.model.layers.0.mlp.fc1.base_layer.bias", "base_model.model.model.layers.0.mlp.fc2.base_layer.weight", "base_model.model.model.layers.0.mlp.fc2.base_layer.bias", "base_model.model.model.layers.0.input_layernorm.weight", "base_model.model.model.layers.0.input_layernorm.bias", "base_model.model.model.layers.1.self_attn.q_proj.base_layer.weight", "base_model.model.model.layers.1.self_attn.q_proj.base_layer.bias", "base_model.model.model.layers.1.self_attn.k_proj.base_layer.weight", "base_model.model.model.layers.1.self_attn.k_proj.base_layer.bias", "base_model.model.model.layers.1.self_attn.v_proj.base_layer.weight", "base_model.model.model.layers.1.self_attn.v_proj.base_layer.bias", "base_model.model.model.layers.1.self_attn.dense.base_layer.weight", "base_model.model.model.layers.1.self_attn.dense.base_layer.bias", "base_model.model.model.layers.1.mlp.fc1.base_layer.weight", "base_model.model.model.layers.1.mlp.fc1.base_layer.bias", "base_model.model.model.layers.1.mlp.fc2.base_layer.weight", "base_model.model.model.layers.1.mlp.fc2.base_layer.bias", "base_model.model.model.layers.1.input_layernorm.weight", "base_model.model.model.layers.1.input_layernorm.bias", "base_model.model.model.layers.2.self_attn.q_proj.base_layer.weight", 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```

Comments:
cc @younesbelkada 

Hi @deshwalmahesh 
Thanks for the issue! 
Just to confirm, what PEFT version do you have ? Can oyou try out with the latest PEFT ? `pip install -U peft`

@younesbelkada  I'm using `-U` for everything while installing. Actually I'm using Multi GPU, single Node with Deepspeed Zero-2 with accelerate. I'm also using Checkpointing  and Gradient Accumulation. This could cause problem?

Yes this might be the issue 
We just merged: https://github.com/huggingface/peft/pull/1489 in PEFT that adds official working scripts for PEFT + DS, precisely: https://github.com/huggingface/peft/tree/main/examples/sft can you try them out? 

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.