I was trying to fine tune a model using lora in my Macbook Pro M1 Max

device_map = "mps"
torch.set_default_device(device_map)

# Load dataset (you can process it here)
dataset = load_dataset(dataset_name, split="train")

# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=use_4bit,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=use_nested_quant,
)

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map=device_map,
)
model.config.use_cache = False
model.config.pretraining_tp = 1

# Set supervised fine-tuning parameters
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    peft_config=peft_config,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    tokenizer=tokenizer,
    args=training_arguments,
    packing=packing,
)

# Train model
trainer.train()

The code returns error

TypeError                                 Traceback (most recent call last)
Cell In[10], line 79
     76 DEVICE = "mps"
     78 # Train model
---> 79 trainer.train()

File ~/miniforge3/envs/llm_tuning/lib/python3.11/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1534     self.model_wrapped = self.model
   1536 inner_training_loop = find_executable_batch_size(
   1537     self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1538 )
-> 1539 return inner_training_loop(
   1540     args=args,
   1541     resume_from_checkpoint=resume_from_checkpoint,
   1542     trial=trial,
   1543     ignore_keys_for_eval=ignore_keys_for_eval,
   1544 )

File ~/miniforge3/envs/llm_tuning/lib/python3.11/site-packages/transformers/trainer.py:1656, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1654         model = self.accelerator.prepare(self.model)
   1655     else:
-> 1656         model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
   1657 else:
   1658     # to handle cases wherein we pass "DummyScheduler" such as when it is specified in DeepSpeed config.
...
TypeError: device() received an invalid combination of arguments - got (NoneType), but expected one of:
 * (torch.device device)
      didn't match because some of the arguments have invalid types: (!NoneType!)
 * (str type, int index)

I tried various methods but it kept giving the error