How to Convert JSON to JSONL for OpenAI Fine-Tuning

Fine-tuning OpenAI's models can help you customize the behavior of the model to better suit your specific use case. One common task when preparing data for fine-tuning is converting JSON data into a format known as JSONL (JSON Lines). This format is particularly useful when working with OpenAI’s fine-tuning API because it stores each data entry as a single line, making the model training process more efficient.

In this guide, we’ll walk you through the process of converting a JSON dataset into JSONL format using a New York Giants sports team example. This will allow you to create a dataset that can be used to fine-tune a model that provides sports-related information.

What is JSONL?

JSONL stands for JSON Lines, a file format where each line is a separate JSON object. This structure makes it easy to read and process large datasets in a line-by-line fashion, which is perfect for tasks such as model fine-tuning. The OpenAI fine-tuning API expects data in JSONL format, where each line represents a separate interaction between the user and the assistant.

Example Data Structure for Fine-Tuning

When using OpenAI’s fine-tuning API, the data needs to follow a specific structure. The key elements of the JSONL format are:

• messages: An array of messages that represent the conversation between the system, user, and assistant.
• role: Defines who is sending the message (system, user, or assistant).
• content: The content of the message.
• weight (optional): Indicates the importance of the assistant’s response (usually set to 1 for most use cases).

Here’s a typical example of the format:

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Example: Creating a Dataset for the New York Giants

Let’s say you want to create a dataset where users can ask questions about the New York Giants, and the assistant will provide informative answers. Below is an example of the JSON structure that represents interactions between a user and the assistant:

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In this case, the user asks about the Super Bowl victories of the New York Giants, and the assistant provides two responses: a more detailed preferred output, and a shorter non-preferred output.

Converting JSON to JSONL

To fine-tune OpenAI’s models, we need to convert this JSON data into JSONL format. The key is ensuring that each line contains a complete conversation with the necessary system, user, and assistant roles, structured appropriately.

Steps to Convert JSON to JSONL

1. Identify the Components: The input JSON data contains an array of messages and separate preferred_output and non_preferred_output fields. These need to be combined into a single conversation.

2. Format Each Entry: Each line in the JSONL file must represent a full conversation, including the system, user, and assistant messages.

Here’s what the converted JSONL file will look like:

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Key Points:

• Each line contains a single conversation with a system, user, and assistant message.
• The weight attribute is added to the preferred_output response to indicate that it is the preferred response (you can adjust the weight based on the quality of the responses).
• The non_preferred_output is included as an alternative, shorter response from the assistant.

Automating the Conversion with Python

If you have a larger dataset, manually converting it to JSONL can be time-consuming. You can automate the process with a Python script. Below is a Python script that reads the input JSON file and converts it into JSONL format:

Python Script for Conversion

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How to Use the Python Script:

1. Save the input JSON data in a file named input.json.

2. Save the script as convert_json_to_jsonl.py.

3. Run the script using Python:

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This script will generate an output.jsonl file, where each line corresponds to a conversation about the New York Giants, complete with the system, user, and assistant messages.