Getting Started

Thank you for taking interest in LangDrive!

Langdrive's set of connectors and services makes training LLMs easy! You can get started with just a CSV file, but by providing a Hugging Face API key you can train models and host them directly in the cloud 😉

Import and use Langdrive modules in your scripts or configure and execute Langdrive directly from the CLI. The remainder of this article will explore using both approaches for training and deploy models with langdrive. Along the way we will explore the use of a YAML doc to help with the connecting to data and services.

Using the CLI

Node developers can train and deploy a model in 2 simple steps.

1. npm install langdrive --save-dev
2. langdrive train --csv ./path/to/csvFileName.csv --hfToken apikey123 --deploy --hfModelPath username/new-model-name

In this case, Langdrive will retrieve the data, train a model, host it's weights on Hugging Face, and return an inference endpoint you may use to query the LLM. Here's a working sample dataset.

The command langdrive train is used to train the LLM, please see how to configure the command below.

CLI Arguements:

• 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.
• 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
• csv: Path to shared csv training data.
• path: Path to shared training data.
• inputPath: Path to input training data if not shared.
• outputPath: Path to output training data if not shared.
• inputValue: Input value to extract from training data. default: input
• outputValue: Output value to extract from training data. default: output

It is assumed you do not want to deploy your model if you run langdrive train. In such a case a link to where you can download the weights will be provided. Adding --deployToHf will return a link to the inferencing endpoint.

More information on how to ingest simple data using the CLI can be found in the CLI docs. For more complex examples, read on...

Getting Started with YAML

Getting the data and services you need shouldn't be the hardest part about training your models! Using YAML, you can configure more advanced data retrieval, processing, and training/ deployment strategies. Once configured, these settings are available for the standalone API and also from the CLI.

For yaml, langdrive reads the yaml configs from a file called langdrive.yaml from the root directory by default.

The information covered in this subsection is included in this Sample YAML doc. Refer to the Yaml docs for more information.

Step 1: Configure Your Data Connectors

Our growing list of data-connectors allow anyone to retrieve data through a simple config doc. As LangDrive grows, our set of Open-Source integrations will grow. At the moment, you can connect to your data using our email, firestore, and gdrive classes.

In essense, config of these data-connectors is as straight forward as:

langdrive.yaml

firestore: clientJson: "secrets/firebase_service_client.json" databaseURL: "env:FIREBASE_DATABASE_URL"

drive: appType: "desktop" clientJson: "secrets/drive_service_client.json" scopes:

• "https://www.googleapis.com/auth/drive"
• "https://www.googleapis.com/auth/drive.metadata.readonly"

email: password: env:GMAIL_PASSWORD email: env:GMAIL_EMAIL

You may specify .env variables using env: as a prefix for your secret information.

In our example above, the firestore clientJson attribute is a Firebase service account file, the drive clientJson is from the Cloud Console, and the password attribute in the email config is for an app password.

Once this information is provided, the entire OAuth Process will automatically be handled on your behalf when using any associated library, regardless if it's used in the CLI or API. Please refer to our notes on security for more information on the Outh2 process when using google.

Step 2: Configure Your LLM Tools

Once you have your data-connectors set up, config your training and deployment information.

Training on Hugging Face and hosting the weights on Hugging Face hubs:

langdrive.yaml

huggingface: hfToken: env:HUGGINGFACE_API_KEY deployToHf: false

Heres how you could specify the model you want to train, and where to host it:

huggingface: hfToken: env:HUGGINGFACE_API_KEY baseModel: vilsonrodrigues/falcon-7b-instruct-sharded trainedModel: karpathic/falcon-7b-instruct-tuned deployToHf: true

Simple enough, huh? Here comes the final step.

Step 3: Connecting Your Data to Your LLM

To connect data to your LLM tool, we will need to create a new YAML entry train.

Here we set the labels of data we want to train on. In the case of a CSV, a most simple example, we can use the path value to specify it's location.

If the csv has an 'input' and 'output' column, all you need to do is specify the path.

langdrive.yaml

train: path: ../shared.csv

But just in case, you can set the columns too.

train: path: ../shared.csv inputValue: inputColumnName - Attribute to extract from path outpuValue: outputColumnName

You can even specify which datasets to use; they must have equal length entries, however.

train: input: path: ../input.csv value: inputColumnName output: path: ../output.csv value: outputColumnName

Now lets show how to query data from one of those third-party services we configured earlier.

To do this, set a service and query within the train entry. Begin by setting the name of a data-connector as the service and one of its methods (and its args) as the query value. Users are encouraged to explore the service documentation to find available methods and the parameters they take. Once the service, method, and parameters are known.

train: service: gdrive query: getCsvByName: filename: midjourney_prompt.csv mimeType: text/csv directory: false directoryId: false input: value: input output: value: output

In the example we use the getCsvByName method from Langdrive's gdrive class, passing arguements as specified in the Langdrive GDrive docs. filename is the filename to retrieve data from. mimeType and directory/directoryId can optionally be used to speed up the file search.

Note: Much like path parameter in the examples above, the service and query attributes can be place at the root of the train entry, or nested within an input and output child entry.

Lets take a look at another example.

train: service: firestore query: filterCollectionWithMultipleWhereClauseWithLimit: collection: chat-state
filterKey: [] filterData: [] operation: [] limit: 5 input: value: chat.0.content - retrieved nested Json Objects data output: value: chat.1.content

In the example, we use the filterCollectionWithMultipleWhereClauseWithLimit method from Langdrive's Firestore class, passing arguements as specified in the Langdrive Firestore docs. collection is the firestore collection name to retrieve data from, (limited to the first 5 entries). filterKey and filterData are not specified in this example but contain the field name/key to filter. The operation value specifies the firebstore query operator to use (For example, ==, >=, <= ).

Gettings Started with API:

Import Langdrive in your next project

import langdrive

or

and for select classes: [gdrive]

Our classes can be exposed in the typical manner. For more information on any one class, please refer to it's corresponding documentation.

Coming Soon: Deploy self-hosted cloud based training infrastructure on AWS, Google Cloud Platform, Heroku, or Hugging Face. Code is currently being used internally and is under development prior to general release - code avaialbe in-repo under /src/train.

If you would like to interact directly directly with our training endpoint you can call our hosted training image directly via the langdrive API.

Endpoint: POST https://api.langdrive.ai/train

You can test the quality of one of these models by visit the Langdrive Playground

Request Body

The request accepts the following data in JSON format.

{
   "baseModel": "string",
   "hfToken": "string",
   "deployToHf": "Boolean",
  "trainingData": "Array",
   "hfModelPath": "string",
}

baseModel: The original model to train. This can be one of our supported models, listed below, or a Hugging Face model

• Type: String
• Required: Yes

hfToken: Your Hugging Face token with write permissions. Learn how to create a Hugging Face token here.

• Type: String
• Required: Yes

deployToHf: A boolean representing whether or not to deploy the model to Hugging Face after training

• Type: Boolean
• Required: Yes

trainingData: This is an array of objects. Each object must have two attributes: input and output. The input attribute represents the user’s input and output attribute represents the model’s output.

• Type: Array
• Required: Yes

hfModelPath: The hugging face model repository to deploy the model to after training is complete. This path must exist before being used

• Type: String
• Required: No

Response Body

The request returns the following data in JSON format.

HTTP/1.1 200
Content-type: application/json
{
   "success": "true",
}

Model Training

We plan to expand the number of available models for training. at the moment only sharded models work as using PEFT is how these models are trained.

Models Support Matrix

Causal Language Modeling

Model	Supported
Falcon-7b-sharded	✅
GPT-2	Comming Soon
Bloom	Comming Soon
OPT	Comming Soon
LLaMA	Comming Soon
ChatGLM	Comming Soon

Model Type Support

Model Type	Support
Conditional Generation	✅
Conditional Generation	✅
Sequence Classification	✅
Token Classification	✅
Text-to-Image Generation
Image Classification
Image to text (Multi-modal models)
Semantic Segmentation