What does the validator check?

Per line it confirms valid JSON, the presence of a messages array, allowed roles for the chosen provider, non-empty content, and at least one assistant turn. It also warns on lines whose rough token estimate is very large, and on datasets with fewer than 10 examples.

How accurate is the token-length warning?

It is a rough estimate (about four characters per token) used only to flag lines that may exceed a model's context window. For exact counts use a model-specific tokenizer; the warning is a safety net, not a precise measurement.

What is the difference between the OpenAI and Anthropic checks?

OpenAI fine-tuning uses a messages array with system, user and assistant roles. Anthropic's format also uses a messages array but only allows user and assistant roles, with any system instruction supplied separately. The validator enforces the right rule set per provider.

Is my dataset uploaded anywhere?

No. Parsing and validation run entirely in your browser. Nothing you upload or paste is sent to a server, stored or logged.

.jsonl File Validator

Catch dataset errors before they fail your job

A single malformed line can sink an entire fine-tuning job after you have already uploaded and waited. This validator checks every line of your .jsonl file for valid JSON, correct structure and the right roles for your provider, and flags long examples — all locally, before you upload.

How validation works

JSONL stores one independent JSON object per line. The validator parses each line separately so a single bad line does not hide the rest, then applies schema rules for your chosen provider:

Valid JSON — the line must parse cleanly on its own.
A messages array — present and non-empty.
Allowed roles — OpenAI permits system, user, and assistant; Anthropic permits only user and assistant (system is passed separately).
Non-empty content and at least one assistant turn, since that is what the model learns to produce.

It also computes a rough token estimate per line to warn about examples that may blow past the model’s context window, and checks that the dataset has the minimum number of examples a fine-tuning job requires.

Tips

Fix the first error first. A stray comma or unescaped quote often explains several downstream failures.
Match roles to your provider. Moving an OpenAI dataset to Anthropic means lifting the system message out of the array.
Mind the long lines. A handful of oversized examples can be truncated silently during training — trim or split them.
Re-validate after every edit so a quick fix does not introduce a new problem.