# Load dataset (you can process it here)dataset = load_dataset(dataset_name, split="train")# Load tokenizer and model with QLoRA configurationcompute_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,)# Check GPU compatibility with bfloat16if compute_dtype == torch.float16 and use_4bit:    major, _ = torch.cuda.get_device_capability()    if major >= 8:        print("=" * 80)        print("Your GPU supports bfloat16: accelerate training with bf16=True")        print("=" * 80)# Load base modelmodel = AutoModelForCausalLM.from_pretrained(    model_name,    quantization_config=bnb_config,    device_map=device_map)model.config.use_cache = Falsemodel.config.pretraining_tp = 1# Load LLaMA tokenizertokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_tokentokenizer.padding_side = "right"# Load LoRA configurationpeft_config = LoraConfig(    lora_alpha=lora_alpha,    lora_dropout=lora_dropout,    r=lora_r,    bias="none",    task_type="CAUSAL_LM",)# Set training parameterstraining_arguments = TrainingArguments(    output_dir=output_dir,    num_train_epochs=num_train_epochs,    per_device_train_batch_size=per_device_train_batch_size,    gradient_accumulation_steps=gradient_accumulation_steps,    optim=optim,    save_steps=save_steps,    logging_steps=logging_steps,    learning_rate=learning_rate,    weight_decay=weight_decay,    fp16=fp16,    bf16=bf16,    max_grad_norm=max_grad_norm,    max_steps=max_steps,    warmup_ratio=warmup_ratio,    group_by_length=group_by_length,    lr_scheduler_type=lr_scheduler_type,    report_to="tensorboard")# Set supervised fine-tuning parameterstrainer = 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 modeltrainer.train()# Save trained modeltrainer.model.save_pretrained(new_model)