> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-ai-studio/services/model-testing-test-jobs/tutorials/how-to-evaluate-a-model.md).

# How to Evaluate a Model?

## Run History

Run history provides a detailed log of all testing runs for a selected model.

<figure><img src="https://fptcloud.com/wp-content/uploads/2025/09/Run-history.png" alt=""><figcaption></figcaption></figure>

**Notice:** Each record in history shows when the job was started

## Run Details

The **Run details** page provides a comprehensive overview of a fine-tuning job. It includes metadata, configuration settings, and metrics.

![](https://fptcloud.com/wp-content/uploads/2025/09/Run-details.png)

You can see:

* **Input:** The input data of the test, for example, a question in a Question Answering task.
* **Ground Truth:** The correct answer (label) corresponding to each input, used for comparison with the predicted result.
* **Output:** The answer that the model produces based on the input.
* **Metrics:** Calculated based on the comparison between output and ground truth.


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