Fine T-Tool for Custom AI Training

Example

Using the OpenAI API, Fine T can guide a user through the process of setting up a fine-tuning job. For instance, configuring a job that fine-tunes a model based on a dataset containing customer support interactions to improve response relevance.

Scenario

A company wants to refine their chatbot to better handle specific customer inquiries based on past interactions. Fine T provides the necessary Python code and parameter explanations to set up this fine-tuning job.

Example

Fine T can demonstrate how to list all active and past fine-tuning jobs using the API, allowing users to monitor job status and retrieve details of specific jobs.

Scenario

An AI research team periodically checks the status of their ongoing fine-tuning jobs to adjust parameters or pause jobs as needed. Fine T assists by providing the exact methods to retrieve this information efficiently.

Example

If a fine-tuning job is no longer needed or was configured incorrectly, Fine T helps users understand how to cancel these jobs through the API, including the necessary commands and parameters.

Scenario

During the development phase, a developer realizes that a job was set with incorrect data. Fine T guides the cancellation process to prevent resource waste and potentially skewed model training.