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Fine-Tuning Dataset Formatter

Turn question and answer pairs into a JSONL training file for OpenAI, Llama or Gemini.

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Quick Tool Overview

The Fine-Tuning Dataset Formatter converts question and answer pairs from labels, spreadsheet columns or text blocks into a JSONL training file for OpenAI chat, ShareGPT, Alpaca, Gemini or prompt and completion, with an optional system prompt and duplicate removal.

What it does
Builds fine-tuning JSONL from Q&A pairs
You give it
Prompt and answer pairs in a common layout
You get back
A training file in the format your trainer expects
Best for
OpenAI fine-tuning, Axolotl, Unsloth and Vertex AI
AI Tools Free, unlimited use No sign-up Runs in your browser

About the Fine-Tuning Dataset Formatter

Most fine-tuning data starts life as a spreadsheet, a FAQ page or a document of questions and answers. Getting it into the exact JSONL shape a trainer expects — escaped correctly, one object per line, with the right keys — is tedious and easy to get subtly wrong.

Paste your pairs as Q: and A: labels, two tab-separated columns copied from a spreadsheet, or blocks with the prompt on the first line. Choose the target format: OpenAI chat messages for GPT fine-tuning, ShareGPT conversations for Axolotl, LLaMA-Factory and Unsloth, Alpaca instruction and output, Gemini contents for Vertex AI, or the legacy prompt and completion format. An optional system prompt is added to every example.

Whitespace is tidied, duplicate prompts are removed so they cannot leak into your validation split, and the token size of each example and of the whole epoch is shown. Examples too long for a typical training context and datasets too small to beat a good prompt are flagged.

Key features of the Fine-Tuning Dataset Formatter

Five training formats

OpenAI chat messages, ShareGPT, Alpaca, Gemini contents and legacy prompt and completion.

Reads spreadsheet data

Two tab-separated columns pasted from Excel or Google Sheets become pairs directly.

System prompt on every row

Train with the exact system prompt you will use in production.

Duplicate removal

Repeated prompts are dropped so they cannot inflate validation scores.

Size warnings

Flags datasets under the minimum and examples too long for common training contexts.

How to use the Fine-Tuning Dataset Formatter

  1. 1

    Paste your pairs

    Q: and A: labels, spreadsheet columns or blank-line blocks are detected.

  2. 2

    Choose the target format

    OpenAI chat, ShareGPT, Alpaca, Gemini or prompt and completion.

  3. 3

    Add a system prompt

    Optional. It is written into every example exactly as it will be used in production.

  4. 4

    Download the JSONL

    Validate it, split off a validation set, and upload it to your trainer.

Frequently asked questions

ShareGPT stores a multi-turn conversation as a list of turns marked human and gpt. Alpaca stores a single instruction, an optional input and an output. ShareGPT suits chat models; Alpaca suits single-turn instruction tasks.

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