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Validate chat fine-tuning data and estimate cost

Validate JSONL conversation structure, error types, dataset statistics, and token cost before producing a clean training file.

Goal

Validate JSONL conversation structure, error types, dataset statistics, and token cost before producing a clean training file.

Validate messages, role/content fields, and unknown keys line by line; classify errors. [source-openai-validate-chat-finetuning-dataset]
Compute counts, lengths, and token estimates, flagging threshold issues. [source-openai-validate-chat-finetuning-dataset]
Rerun validation after fixes and sample-review before an owner decides on upload. [source-openai-validate-chat-finetuning-dataset]

Check JSONL row totals, accepted/rejected counts, error types, and estimated tokens; confirm the cleaned file contains only valid examples.

For rejected rows, use reported line numbers to inspect role order, JSON structure, or message fields; never overwrite the original file.

Python, OpenAI API account, redacted/approved conversation JSONL; do not submit training automatically.

Reads JSONL locally and may call a tokenizer for estimates; uploading files or creating a fine-tuning job sends training data to the provider and may incur charges, so do so only with separate approval.

Run the source on isolated test data, then check outputs and dependency versions before adapting it.

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License: MIT