okay I understand but I am just using T5 model form the library so it is not like my own model or so. I can post the code anyways.
import torch
import argparse
import os
import sys
import numpy as np
import torch.nn.functional as F
sys.path.append('..')
from transformers import T5ForConditionalGeneration, Trainer, TrainingArguments
from data_reader import GetDataAsPython
from sklearn.model_selection import train_test_split
from prepare_data import create_data, create_dataset
from transformers import T5Tokenizer

parser = argparse.ArgumentParser()
parser.add_argument('-e', '--epochs', type=int, default=100)
parser.add_argument('-bs', '--batch-size', type=int, default=1)
parser.add_argument('-lr', '--learning-rate', type=float, default=1e-4)
parser.add_argument('-gcv', '--gradient-clip-val', type=float, default=0.0)
parser.add_argument('-wd', '--weight-decay', type=float, default=0.01)
args = parser.parse_args()

# delete the logs directory
model_name = "t5"
os.system("rm -rf ./logs" + model_name)
os.system("rm -rf ./results_" + model_name)

data = GetDataAsPython('../data_large.json')

train_inputs, train_labels, val_inputs, val_labels, test_inputs, test_labels = create_data(data, ['no-array-constructor'])

# from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained('t5-small')
print('len of tokenizer before adding: ', len(tokenizer))
tokenizer.add_tokens(['{', '}', '<', '>'])
train_dataset = create_dataset(train_inputs, train_labels, tokenizer, True)
val_dataset = create_dataset(val_inputs, val_labels, tokenizer, False)
test_dataset = create_dataset(test_inputs, test_labels, tokenizer, False)


def compute_val_metrics(eval_predictions):
    # print('\n')
    # print(len(eval_predictions.predictions[1]))
    # print(len(eval_predictions.predictions[1][0]))
    # print(eval_predictions.predictions[1][0][0].shape)

    return metrics

training_args = TrainingArguments(
    output_dir='./results_' + model_name,          
    num_train_epochs=args.epochs,              
    per_device_train_batch_size=args.batch_size,  
    per_device_eval_batch_size=4,   
    warmup_steps=500,                
    weight_decay=args.weight_decay,               
    logging_dir='./logs_' + model_name,
    logging_steps=10,
    do_eval=True,
    evaluation_strategy='epoch',
    learning_rate=args.learning_rate,
    load_best_model_at_end=True,
    metric_for_best_model='eval_loss',
    greater_is_better=False,
    # prediction_loss_only=True
)

model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True)
model.resize_token_embeddings(len(tokenizer))
# model.resize maybe depending on tokens

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    optimizers=[torch.optim.Adam(params=model.parameters(), lr=args.learning_rate), None],       
    tokenizer=tokenizer,
    compute_metrics=compute_val_metrics
)

trainer.train()