# How to evaluate a tranied model?

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**Category:** Models\
**Created:** [September 4, 2025, 8:50am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-evaluate-a-tranied-model%2F168094 "2025-09-04T08:50:52Z")\
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**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [September 5, 2025, 1:08pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-evaluate-a-tranied-model%2F168094%2F4 "2025-09-05T13:08:40Z")

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[When it comes to step-by-step values, I think the standard approach is to log them during training](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrainer-doesnt-show-the-loss-at-each-step%2F542)%2C like below. While it’s possible to do it afterward, the [code becomes significantly more complicated](https://github.com/huggingface/trl/issues/1626)…

```py
args = DPOConfig(
    output_dir="dpo-eval-demo",
    do_train=True, # training must run to log stepwise eval
    do_eval=True,
    evaluation_strategy="steps",
    eval_steps=100,
    logging_strategy="steps",
    logging_steps=10,
    report_to="tensorboard", # or "wandb"
    logging_dir="tb_logs",
    generate_during_eval=False,
    reference_free=True,
)
trainer = DPOTrainer(model=policy, args=args, train_dataset=your_train, eval_dataset=eval_ds, processing_class=tok)
trainer.train()

```

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