# save embedding

The `save embedding` command saves trained model embeddings and their vocabulary to a vector table. The saved weights may be used as pretrained embeddings while training a downstream model or may be defined as dataset features from the vector database source using functions such as top-k nearest.

## Syntax

{% code overflow="wrap" %}
```
save embedding <model name>,<run id>,<vector table name> 
```
{% endcode %}

## Parameters

| Parameter             | Description                                                                    |
| --------------------- | ------------------------------------------------------------------------------ |
| `<model name>`        | The name of the ML model                                                       |
| `<run id>`            | The specific training run identifier                                           |
| `<vector table name>` | The vector database table name in which to store the embeddings and vocabulary |

## Example

{% code overflow="wrap" %}
```
save embedding trip_fare_2_ml_model,1,faredata
```
{% endcode %}
