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

# Evaluate

> Run evaluation on a trained Luna metric.

Use this when you already have a trained Luna Metric and want to evaluate it on a separate dataset.

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

metrics = run_evaluation(
    model_output_dir="./luna_models/enter-your-lora-weights-directory-here",
    csv_path="./eval.csv",
    metric_type="boolean",  # or "multi_class"
    label_column="label",
    class_names=None,       # required for multi_class
    # base_model_path="/path/to/base/model",  # optional, in case your base model is also stored in a directory, otherwise we'll try to pull from HF
)
```

## Required artifact files

* `prompt_template.txt` (generated by this SDK)
* Access to HF to pull the base model (Llama 3B/8B etc) or have the base model in another dir (`base_model_path`)
* Luna metric adapter weight files in a local directory (generated by this SDK)

## Dataset requirements

* CSV must contain:
  * all prompt variables used by `prompt_template.txt`
  * the configured `label_column`