# Model Types & Metadata

## Model Parameters

To store Models in the Models list, add the following Model-specific parameters to the Experiment command when running an Experiment.

#### Model Type

`--modelType` defines the type of model that is being generated by the experiment. For example, `--modelType Tensorflow` will ensure that the model checkpoint files being generated are recognized as TensorFlow model files.

| Model Type Values | Description                                     |
| ----------------- | ----------------------------------------------- |
| `"Tensorflow"`    | TensorFlow compatible model outputs             |
| `"ONNX"`          | ONNX model outputs                              |
| `"Custom"`        | Custom model type (e.g., a simple Flask server) |

## Custom model metadata

When `modelType` is not specified, custom model metadata can be associated with the model for later reference by creating a **gradient-model-metadata.json** file in the `modelPath` directory. Any valid JSON data can be stored in this file.

For models of type `Tensorflow`, metadata is automatically generated for your experiment, so any custom model metadata will be ignored.

An example of custom model metadata JSON is as follows:

```javascript
{
  "metrics": [
    {
      "name": "accuracy-score",  
      "numberValue": 60
    }
  ]
}
```