> ## Documentation Index
> Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Fine-tune a Luna evaluator from a labelled dataset.

Training is the final step before your Luna evaluator is ready.

You run it using:

```python theme={null}
from galileo_luna_ft.training import run_training

training_stats = run_training(config_path="./config.yaml")
```

## Inputs Required

1. Config
2. Labelled dataset
3. GPU / training environment

## What it does

1. Reads your `training` config section
2. Loads the labelled dataset
3. Fine-tunes a LoRA adapter for your Luna evaluator
4. Evaluates baseline vs fine-tuned performance
5. Writes model artifacts locally and/or to configured destinations

## Key concepts

* **Evaluator type**: `boolean` or `multi_class` ([Read More](/luna-studio/sdk/how-to-train-your-luna-evaluator/training/config/evaluator-output-types))
* **Prompt template**: defines the target response format for the model
* **Model configuration**: base model plus LoRA settings

## Output artifacts

Artifacts are written under:

`{training.output.local_path}/{training.output.model_name}/`

Common outputs include:

* model + Tokenizer files
* `prompt_template.txt`
* `training_metrics.json`
* `plots/`

See [Output artifacts](/luna-studio/sdk/how-to-train-your-luna-evaluator/training/output-artifacts) for more detail.

## Test a trained Luna Evaluator

After training, you can run evaluation again on a different test set.

See [Evaluate](/luna-studio/sdk/how-to-train-your-luna-evaluator/training/evaluate).

## Next: configure training

See the detailed [Training config reference](/luna-studio/sdk/how-to-train-your-luna-evaluator/training/config/config).