How do I compute validation loss for a fine-tuned Qwen model in Hugging Face Transformers during evaluation?

I trained a Qwen model on my own dataset. Now I need to evaluate my trained model using the loss function, but I don’t know how to do it. I saw examples for other metrics such as accuracy and precision, but how can I evaluate the model using the loss function? I need to plot the different loss functions to evaluate which training session was the best. I have prepared my own dataset for it, but I don't know how I should carry one.

training_args = DPOConfig(
    output_dir=logging_dir,
    logging_steps=10,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    loss_type=["sft"],  
    loss_weights=[1.0],  
    max_prompt_length = 512,
    max_completion_length = 512,
    num_train_epochs=100,
    max_steps=100000,
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    save_strategy="steps",
    save_steps=25000,
    eval_strategy="steps",
    eval_steps=100,
    
)

trainer = DPOTrainer(
    model=model,
    processing_class=tokenizer,
    args=training_args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['valid'],
)

trainer.train()