Skip to main content

Command Line

Simply:

  1. Install Langdrive: npm isntall langdrive
  2. Train a model: `langdrive train` + [...Args]`

Here are your Args:

  • yaml: Path to optional YAML config doc, default Value: './langdrive.yaml'. This will load up any class and query for records and their values for both inputs and ouputs.
  • csv: Path to training data. The training data should be a two-column CSV of input and output pairs.
  • hfToken: An API key provided by Hugging Face with write permissions. Get one here.
  • baseModel: The original model to train. This can be one of the models in our supported models shown at the bottom of this page
  • deploy: Weather training weights should be hosted in a hosting service. Default False.
  • deployToHf: Whether traiing weights should be stored in huggingface specifically. Either true | false
  • hfModelPath: The full path to your hugging face model repo where the model should be deployed. Format: hugging face username/model
  • inputValue: The input value to extract from the data retrieved, default: 'input'
  • outputValue: The output value to extract from the data retrieved, default: 'output'

CLI args are parsed as YAML when running commands.

this is a non-exhaustive list of valid operations

langdrive train 
langdrive train --yaml "../pathToYaml.yaml"
langdrive train --hfToken 1234 --csv "../shared.csv"
langdrive train --hfToken 1234 --csv ../shared.csv --inputValue "inputColname" --outputValue "colname" 
langdrive train --csv "./tests/midjourney_prompt.csv" --deploy