I've been Verifying settings while fine-tuning the dataset... After a long time, the cursor kept flashing

[PiaoGengHan]

Here I used the dataset given by git as a test for the initial fine-tuning, but there was no response, and then I thought that the dataset might be too large, resulting in too long a time to split the validation set and the training set, I did some datasets according to his format for testing, or stuck in Verifying settings... No response for a long time, here is the log from the command line. I wonder if something went wrong with the settings. Thank you very much for your answers!!

Please describe yourAIGC\litgpt\litgpt>litgpt finetune microsoft/phi-2 --data JSON --data.json_path my_custom_dataset.json --data.val_split_fraction 0.1 --out_dir out/custom-model
{'access_token': None,
'checkpoint_dir': WindowsPath('checkpoints/microsoft/phi-2'),
'data': JSON(json_path=WindowsPath('my_custom_dataset.json'),
mask_prompt=False,
val_split_fraction=0.1,
prompt_style=<litgpt.prompts.Alpaca object at 0x0000023E1F336440>,
ignore_index=-100,
seed=42,
num_workers=4),
'devices': 1,
'eval': EvalArgs(interval=100,
max_new_tokens=100,
max_iters=100,
initial_validation=False,
final_validation=True,
evaluate_example='first'),
'logger_name': 'csv',
'lora_alpha': 16,
'lora_dropout': 0.05,
'lora_head': False,
'lora_key': False,
'lora_mlp': False,
'lora_projection': False,
'lora_query': True,
'lora_r': 8,
'lora_value': True,
'num_nodes': 1,
'optimizer': 'AdamW',
'out_dir': WindowsPath('out/custom-model'),
'precision': None,
'quantize': None,
'seed': 1337,
'train': TrainArgs(save_interval=1000,
log_interval=1,
global_batch_size=16,
micro_batch_size=1,
lr_warmup_steps=100,
lr_warmup_fraction=None,
epochs=5,
max_tokens=None,
max_steps=None,
max_seq_length=None,
tie_embeddings=None,
max_norm=None,
min_lr=6e-05)}
Using bfloat16 Automatic Mixed Precision (AMP)
Seed set to 1337
Number of trainable parameters: 2,621,440
Number of non-trainable parameters: 2,779,683,840
The longest sequence length in the train data is 363, the model's maximum sequence length is 363 and context length is 2048
Verifying settings ...

[PiaoGengHan]

Here is the device information:
Device name DESKTOP-VAT73EM
Processor Intel(R) Core(TM) i9-14900KF 3.20 GHz
Band RAM 64.0 GB (63.8 GB available)
Graphics card 4090
System type 64-bit operating system, x64-based processor
Disk 2T SSD + 10T HDD
Tuned model microsoft/phi-2