JSONL validator and CSV to JSONL converter

A fine-tuning file is JSON Lines: every line is one complete JSON object, and each object is one training example. Providers check the file only after you upload it, and some problems never fail at all: an example with no answer or one that is too long just wastes training. This tool runs the checks first, on your own device.

What it checks

Every line is parsed on its own. If a line isn’t valid JSON, you get the line and column and, where it can tell, the likely cause: a trailing comma, single quotes, Python’s True or None, or an object pretty-printed over several lines. Blank lines, a byte order mark and a file saved as a JSON array are flagged too. Windows line endings are fine: JSON Lines allows them.

For the OpenAI chat format each line needs a messages list with the roles system, user, assistant and tool. The validator reports a missing assistant answer, empty content, a system message after the conversation has started, two user turns in a row, a conversation that ends after the last answer, weight values other than 0 or 1, and tool calls whose arguments aren’t a JSON string. OpenAI’s DPO lines (input, preferred_output, non_preferred_output) are recognised as well. Mistral and most open-source trainers read the same shape, so this check covers their files too.

For Gemini on Google Cloud each line needs a contents list with user and model turns, text inside parts, and an optional systemInstruction that holds only text. The most common mistakes are OpenAI habits: the role assistant instead of model, a system turn inside contents, and content instead of parts.

Generic JSONL checks only that every line is valid JSON and that the lines share the same keys, which catches a renamed column or a stray record in any dataset.

Tokens and limits

With “Estimate tokens” on, each example is counted with OpenAI’s o200k_base tokenizer. For OpenAI files the count follows the formula in OpenAI’s cookbook: 3 tokens of framing per message plus the role and content, and 3 more per example. You see the total, the median and the largest examples by line. Training tokens are that total times the number of epochs, so a training cost estimate is one multiplication away. Gemini counts its tokens differently, so treat its numbers as a guide.

The limits table compares your file with each provider’s documented limits, checked on 2026-10-12. OpenAI needs at least 10 examples, suggests starting with 50, and truncates examples over 65,536 tokens from the end, which is where the answer is. Google recommends starting with 100 examples and allows 131,072 tokens per example.

CSV to JSONL

The converter reads CSV the way spreadsheet programs write it: quoted cells, doubled quotes, commas and line breaks inside cells, comma, semicolon or tab separators, and the byte order mark Excel adds. Each row becomes one example. Line breaks inside a cell are kept as \n in the JSON, spaces around a cell are trimmed, and rows with an empty prompt or answer are skipped and listed so you can fix them.

Large files and privacy

Files are read and checked in a Web Worker, a background thread in your browser, so a file of tens of megabytes doesn’t freeze the page, and a progress bar shows how far it has got. Nothing is uploaded: there is no server step, and the tool works the same offline once the page has loaded.