Visual Fine-Tune Dataset Editor for JSONL
Fine-tune dataset editor. Author chat-format JSONL visually, edit messages with proper roles, switch between OpenAI, Anthropic, Gemini and ShareGPT shapes on export. Browser-only.
100% client-side. No upload — your training rows never leave this tab.
Edit
Fine-Tune Dataset Editor
A visual editor for the JSONL files that fine-tuning APIs eat. Add a row, type the system prompt, the user turn, the assistant turn, and the next user turn — done. The editor stores rows in a vendor-neutral internal model and exports to OpenAI, Anthropic, Gemini, Llama, Mistral, or ShareGPT shapes with one click. Use it to author training data from scratch, or to import an existing JSONL, fix some rows, and re-export. 100% in-browser.
— S., [email protected]
What this saves you from
- Quoting bugs. Hand-editing JSONL is a minefield of escape sequences and missing braces. The editor produces valid JSON every time.
- Wrong role names. Anthropic uses
user/assistant; Gemini wantsuser/model; ShareGPT prefershuman/gpt. Pick the export format and the editor renames roles correctly. - Format conversions. Have OpenAI-shape data but need ShareGPT? Import, change the export dropdown, export. Done.
- Alternation rules. Anthropic requires strict user/assistant alternation. The editor warns you in real time if a row violates it.
Tips
- System prompt is per-row. A common pattern is a shared system prompt across the whole dataset — author once and the editor copies it to new rows.
- Multiple turns are fine. Add as many user/assistant pairs per row as your conversation needs. Most providers handle multi-turn examples.
- Validate after export. Pipe the exported file through the matching vendor validator (OpenAI / Anthropic / Gemini / Llama / Mistral) for a final pre-flight.