Supports JSONL (one object per line) or JSON array
Ready to analyze
Paste your dataset and click Run Checklist// review dataset structure, balance & duplication before training
Review your fine-tuning dataset for structure, formatting, balance, and duplication issues before training. Free, browser-based, no upload required.
Ready to analyze
Paste your dataset and click Run ChecklistCopy your JSONL or JSON array dataset into the input box.
Choose JSONL prompt/completion, chat messages, or JSON array format.
Click Run Checklist and review the quality score, stats, and issue details.
The Fine-Tuning Data Checklist helps AI/ML engineers and developers validate training datasets before submitting to fine-tuning APIs like OpenAI, Together AI, or Hugging Face. It runs entirely in your browser — no data is ever uploaded or stored.
The tool supports JSONL with prompt/completion keys (OpenAI classic format), JSONL with messages array (chat format), and standard JSON arrays of objects.
No. All analysis happens entirely in your browser using JavaScript. Your dataset never leaves your device.
The score (0–100) is based on: format validity, duplicate rate, empty field rate, token length balance, and missing key rate. Each check contributes a weighted penalty when issues are found.
Generally, 50–500 high-quality examples can produce meaningful improvements. OpenAI recommends at least 50 examples for chat fine-tuning. More data helps, but quality matters more than quantity.
Token counts are approximated using a simple word/character heuristic (~4 chars per token). For exact counts, use the Token Counter tool with a proper tokenizer.
Two entries are considered duplicates if their prompt (or first user message in chat format) matches exactly after trimming whitespace. Near-duplicates are not currently detected.
Good training data typically has completions that are proportional to prompts. Very short completions (under 10 chars) or extremely long completions (over 2000 chars) are flagged as potential issues.
Yes. While the terminology matches OpenAI's API, the structure checks apply to any JSONL-based fine-tuning dataset. Adjust which keys you expect per your platform's documentation.
When you fine-tune a large language model (LLM), the quality of your training data determines the quality of the final model. Even a well-architected training pipeline can produce poor results if the input dataset has structural issues, imbalances, or duplicates. A fine-tuning data checklist is a set of automated checks that catch these problems before you submit the dataset for training — saving you both time and money.
This tool performs those checks directly in your browser. You paste your JSONL or JSON dataset, select the format, and get an instant quality score alongside a detailed breakdown of any issues found.
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Before submitting a dataset to any fine-tuning API — whether OpenAI, Together AI, Mistral, or Hugging Face — it's worth scanning for these common issues:
prompt and completion. The chat format requires a messages array with objects containing role and content. Missing keys mean the sample is silently skipped or causes an error.JSONL (JSON Lines) is the most common format for fine-tuning datasets. Each line in the file is a separate, valid JSON object. This makes it easy to stream large datasets without loading everything into memory at once.
For OpenAI's legacy completion endpoint, each line looks like:
{"prompt": "Translate to French: Hello world\n\n###\n\n", "completion": " Bonjour le monde\n"}For the newer chat format (used with GPT-3.5 Turbo and GPT-4 fine-tuning), each line uses a messages array:
{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}]}This tool supports both formats, plus standard JSON arrays where the entire dataset is wrapped in a [ ] array.
The answer depends on your task and target model. For simple classification or short-answer tasks, 50–100 high-quality examples can produce measurable improvements. For complex, open-ended generation tasks, you typically want 200–1000+ examples. Beyond raw count, diversity matters: if all your examples cover the same scenario, the model will struggle to generalize.
A common mistake is using low-quality data at scale. 1,000 noisy, poorly-formatted examples will often underperform 100 clean, well-structured ones. Run this checklist before scaling your dataset collection efforts.
Fine-tuning APIs have token limits per training example. OpenAI's gpt-3.5-turbo fine-tuning, for example, has a maximum context length. If your prompt + completion pair exceeds this limit, the example will be truncated or rejected. This tool estimates token counts using a simple heuristic — for exact counts, use a proper tokenizer tool before submitting.
As a rule of thumb: keep your total prompt + completion under 2,048 tokens for most fine-tuning endpoints, unless you specifically need a larger context model.
Beyond automated checks, here are qualitative best practices to review manually:
###, be consistent across all examples.Running this fine-tuning data checklist is a quick first step that takes seconds but can prevent training failures that cost hours and dollars to debug.