For more information about formatting your training data, see Learn how to prepare your dataset for fine-tuning. In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM), and the file must be less than 200 MB in size. Here's an example of the training data format: The OpenAI command-line interface (CLI) includes a data preparation tool that validates, gives suggestions, and reformats your training data into a JSONL file ready for fine-tuning. The training and validation data you use must be formatted as a JSON Lines (JSONL) document in which each line represents a single prompt-completion pair. Your training data and validation data sets consist of input & output examples for how you would like the model to perform. Prepare your training and validation data Optionally, analyze your customized model for performance and fit.Check the status of your customized model.Review your choices and train your new customized model.Optionally, choose advanced options for your fine-tune job.Optionally, choose your validation data.Use the Create customized model wizard in Azure OpenAI Studio to train your customized model.Prepare your training and validation data.The fine-tuning workflow in Azure OpenAI Studio requires the following steps: Open an issue on this repo to contact us if you have an issue.įor more information about creating a resource, see Create a resource and deploy a model using Azure OpenAI. You can apply for access to Azure OpenAI by completing the form at. An Azure subscription - Create one for freeĪccess granted to Azure OpenAI in the desired Azure subscriptionĬurrently, access to this service is granted only by application.
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