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What is JSONL for Fine-Tuning?

JSONL, standing for JSON Lines, is a file format that records JSON data line by line.

In particular, the OpenAI platform utilizes the JSONL format when fine-tuning existing AI models.

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JSONL is not only useful for training AI but also for storing system logs and processing large volumes of data efficiently.


Example of a JSONL File​

In JSONL format, each line contains a separate data entry, as shown below:

Example of a JSONL File
{"name": "John", "age": 30, "city": "New York"}
{"name": "Jane Doe", "age": 25, "city": "Los Angeles"}
{"name": "Chloe", "age": 35, "city": "Paris"}

Why Use JSONL Instead of JSON for AI Training​

There are several reasons to use JSONL (JSON Lines) instead of JSON for fine-tuning:

  1. Ease of Line-by-Line Processing: Each line in a JSONL file is its own JSON object, making it easy to read and write data line by line, which is beneficial for handling large datasets.

  2. Memory Efficiency: JSONL format minimizes memory usage during data processing, as it allows line-by-line processing without loading the entire file into memory at once.

  3. Log File-Like Format: JSONL's format is similar to log files, making it suitable for storing and processing log data or streaming data, with each event or data item recorded on individual lines.

  4. Facilitates Parallel Processing: JSONL files are well-suited for parallel processing with computing resources (processes, threads, etc.).

  5. Scalability: JSONL format allows easy addition of new JSON objects at the end of the file as data grows, whereas files with a single JSON object may require modifying the whole file.


Characteristics of JSONL​

  • Each line is an independent data entry, making it easy to understand.

  • Multiple JSON objects can be stored in a single file.

  • Commonly uses the extensions .jsonl or .ndjson.

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