Free Developer Tool
Fine-Tuning Dataset Formatter
Convert CSV or TSV data into JSONL format for fine-tuning OpenAI, Anthropic, and other LLMs. Auto-detect columns, map fields, validate rows, and download a ready-to-use training file. 100% Browser-Based
Detected columns:
How to Use This Tool
1. Paste or Upload CSV
Paste CSV or TSV data into the text area, or drag and drop a .csv or .tsv file. The parser handles quoted fields, commas inside quotes, and escaped double quotes.
2. Map Columns
Select your Target Format and map each CSV column to the correct JSONL role. Columns named prompt, question, or input are auto-detected as the User field.
3. Download or Copy
Review the preview and validation warnings, then Download .jsonl for a ready-to-upload training file or Copy to Clipboard to paste elsewhere.
Fine-Tuning Format Reference
OpenAI Chat Format
Used for fine-tuning gpt-4o-mini, gpt-3.5-turbo, and other chat models. Each line is a complete conversation with messages array.
{"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]}Anthropic Format
Used for fine-tuning Claude models. The system prompt is a top-level field, while user/assistant turns are in a messages array.
{"system": "...",
"messages": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]}Generic Chat JSONL
Simple prompt/completion pairs for any model or framework that ingests JSONL. Useful for datasets that don't target a specific provider API.
{"prompt": "...", "completion": "..."}Alpaca Format
Popular for open-source instruction-tuned models (LLaMA, Mistral, etc.). Uses instruction, optional input context, and output.
{"instruction": "...",
"input": "...",
"output": "..."}Dataset Quality Tips
| Issue | Why It Matters | Fix |
|---|---|---|
| Empty required fields | Training on blank rows teaches the model nothing | Remove or fill rows flagged by warnings |
| Very long rows | Exceeds context windows; truncated during training | Split into smaller examples |
| Inconsistent style | Mixed formality confuses learned behavior | Normalize tone before fine-tuning |
| Too few examples | OpenAI recommends 50–100 minimum; more is better | Augment with paraphrased or synthetic rows |
Common Use Cases
Customer Support Datasets
Export your support ticket history as CSV and convert it to OpenAI Chat format to fine-tune a model that mirrors your team's tone and domain expertise.
Code Instruction Tuning
Convert code review or Q&A spreadsheets into Alpaca format for open-source models. Map the task description to Instruction and the solution to Output.
Multilingual Translation
Use Generic Chat JSONL to convert bilingual CSV data (source / target columns) into prompt-completion pairs for translation fine-tuning tasks.
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