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finetuna: Fine-tuned PowerShell module to Fine-Tune OpenAI Models

Table of Contents

Introduction

finetuna is designed to simplify the fine-tuning of OpenAI models. Train your models and interact with them—all through PowerShell.

Right now, the awesome functionality is that you can pipe a whole directory of jsonl files into a command that trains a whole new model/bot on those files.

Prerequisites

  • PowerShell (any version)
  • OpenAI API Key or Azure OpenAI API Key

Installation

  1. Set Execution Policy: Run the following command if needed:

    Set-ExecutionPolicy RemoteSigned -Scope CurrentUser

  2. Install from PowerShell Gallery: Run the following command:

    Install-Module -Name finetuna

    Dependencies will be installed automatically.

  3. Get and set your OpenAI API Key: Get/Create your OpenAI API key from https://platform.openai.com/account/api-keys and then set as plain text with $env:OPENAI_API_KEY:

    $env:OPENAI_API_KEY = "sk-fake-T3BlbFJi7vpHiKhyYKy8aUT3Blbk"

    You may also want to put it in your $profile. $env:OpenAIKey also works to provide compatibility with other PowerShell modules.

Demo Notebook

finetuna comes with a demo Jupyter notebook that showcases the main functionalities of the module. The notebook is located in the root directory of the module and is named demo.ipynb.

Launching the Demo Notebook

To launch the demo notebook, use the Start-TuneDemo command:

Start-TuneDemo

This command will attempt to launch the notebook in Visual Studio Code (regular or Insiders edition). If Visual Studio Code is not available, it will fall back to platform-specific launchers such as Jupyter or the default application associated with .ipynb files.

Demo Notebook Contents

The demo notebook covers the following topics:

  1. Setting the provider configuration
  2. Grabbing a tuning file
  3. Checking the validity of the training file
  4. Uploading the file
  5. Checking the uploaded file
  6. Starting a tuning job
  7. Waiting for the job to complete
  8. Retrieving the job details
  9. Retrieving events for a specific fine-tuning job
  10. Retrieving and deleting custom models
  11. Retrieving the default model
  12. Measuring token count for a given text
  13. Getting the current provider configuration

By following along with the demo notebook, you can get a hands-on understanding of how to use finetuna to fine-tune OpenAI models and interact with them through PowerShell.

Manual Quick Start

Train* a whole bot in one line

Use a bunch of json files to train or "fine tune" a model to make a bot. Append allows you to fine-tune an initial model then save that as a new model and train the rest of the files against that.

    Get-ChildItem .\sample\totbot*.jsonl | Create-CustomModel -Append

*** "train" used in a casual manner, as I am not an ML scientist and you probably aren't either because they don't use PowerShell for these tasks 💔 Anyway, now I can chat with my cat.

image

Train a bunch of bots in one line

Drop that append parameter and train a bunch of models with a bunch of jsonl files

    Get-ChildItem .\sample\totbot*.jsonl | Create-CustomModel

Usage

  1. Send Files: Use Send-TuneFile to upload your training files.

    Get-ChildItem .\sample\totbot*.jsonl | Send-TuneFile

  2. Verify File Upload: Use Get-TuneFile to make sure your file was uploaded correctly.

    Get-TuneFile | Select-Object -Last 1

  3. Start Training:

    Once your files are ready, start the training job.

    Get-TuneFile | Out-GridView -Passthru | Start-TuneJob

Command Overview

Command Name Description
Clear-TuneProvider Clears the OpenAI provider configuration for finetuna
Compare-Embedding Calculates the similarity between two embedding vectors
Create-CustomModel An alias for New-TuneModel
Get-Embedding Creates an embedding vector for the given text
Get-TuneFile Retrieves a list or a specific tuning file
Get-TuneFileContent Reads the content of a list or specific tuning file
Get-TuneJob Retrieves a list or details of a specific tuning job
Get-TuneJobEvent Fetches events for a list or specific tuning job
Get-TuneModel Retrieves a list or a specific tuning model
Get-TuneModelDefault Gets the default model that Invoke-TuneChat uses
Get-TuneProvider Retrieves the current OpenAI provider configuration for finetuna
Invoke-TuneChat Initiates a chat session with a tuning model
Invoke-TunedChat Initiates a chat session with any model (alias for Invoke-TuneChat)
Measure-TuneToken Measures the token usage of the provided text
New-TuneModel Creates a new tuning model
Remove-TuneFile Deletes a specific tuning file
Remove-TuneModel Deletes a specific tuning model
Request-TuneFileReview Submits a file to Invoke-TuneChat for improvement suggestions
Send-TuneFile Sends a file for tuning
Set-TuneModelDefault Sets the default model that Invoke-TuneChat will use
Set-TuneProvider Configures the OpenAI or Azure OpenAI service context for finetuna
Start-TuneDemo Launches the finetuna demo notebook
Start-TuneJob Starts a new tuning job
Stop-TuneJob Stops a running tuning job
Test-TuneFile Validates tune files before sending to model for training
Wait-TuneJob Waits for a fine-tuning job to complete

Azure OpenAI Services

finetuna also supports Azure OpenAI services. To use Azure, you need to set up the provider using the Set-TuneProvider command:

$splat = @{
    Provider   = "Azure"
    ApiKey     = "your-azure-api-key"
    ApiBase    = "https://your-azure-endpoint.openai.azure.com/"
    Deployment = "your-deployment-name"
}
Set-TuneProvider @splat

Note that finetuned models can be surprisingly expensive to host on Azure ($3/hr + training costs). Be sure to check the pricing before you start training.

Azure FAQ

  1. Default Quota: The default quota for Azure OpenAI services is relatively low. If you're experiencing limitations, consider requesting an increase in your quota through the Azure portal.

  2. Model Compatibility: If your assistant isn't working as expected, try changing the model in your deployment to ensure your deployment is using a compatible model.

  3. API Versions: Make sure you're using a compatible API version. Check the Azure documentation for the latest supported versions.

  4. Deployment Name: When setting up the OpenAI provider for Azure, ensure you're using the correct deployment name. This is different from the model name and is specific to your Azure OpenAI resource.

  5. Region Availability: Azure OpenAI services may not be available in all Azure regions. Ensure you're using a supported region for your deployment.

Configuration

Persisting OpenAI Provider Configuration

You can persist the OpenAI provider configuration to a JSON file using the Set-TuneProvider command. This allows you to save the configuration for future sessions:

Set-TuneProvider -Provider OpenAI -ApiKey "your-openai-api-key"

Resetting OpenAI Provider Configuration

To reset the OpenAI provider configuration, use the Clear-TuneProvider command:

Clear-TuneProvider

This command will remove the persisted configuration file and reset the configuration to its default state.

Examples

# Upload training files
Get-ChildItem .\sample\totbot*.jsonl | Send-TuneFile

# Start a training job
Get-TuneFile | Out-GridView -Passthru | Start-TuneJob

# Chat with your fine-tuned model
Invoke-TunedChat -Message "Tell me about cats"

Removing Files and Models

Remove Specific File

Use Remove-TuneFile to delete a specific file that you have uploaded.

Get-TuneFile | Where-Object Name -eq "sample_file.jsonl" | Remove-TuneFile

Remove Last Uploaded File

Get-TuneFile | Select-Object -Last 1 | Remove-TuneFile

Remove Specific Model

Use Remove-TuneModel to delete a specific tuning model.

Get-TuneModel | Where-Object Name -eq "sample_model" | Remove-TuneModel

Remove All Models

Get-TuneModel -Custom | Remove-TuneModel -Confirm:$false

Learn more about fine-tuning here: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/fine-tuning-considerations