Hello,
I am using huggingface on my google colab pro+ instance, and I keep getting errors like

RuntimeError: CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 15.78 GiB total capacity; 13.92 GiB already allocated; 206.75 MiB free; 13.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

I dont understand why? My dataset is microscopic (40K sentences), and all I am doing is loading bert-large-uncased and follow along the text classification notebook
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('bert-large-cased')
from datasets import load_dataset, load_metric

metric = load_metric('glue', 'sst2')
model = AutoModelForSequenceClassification.from_pretrained("bert-large-cased", num_labels=2)

my trainer args are super standard

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

batch_size = 16

args = TrainingArguments(
    evaluation_strategy = "epoch",
    save_strategy = "epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    num_train_epochs=5,
    report_to="none",
    weight_decay=0.01,
     output_dir='/content/drive/MyDrive/kaggle/',
    metric_for_best_model='accuracy')

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = predictions[:, 0]
    return metric.compute(predictions=predictions, references=labels)

trainer = Trainer(
    model,
    args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics

)

Am I missing something? Should I change some of the options?
Thanks!!