OpenAI fine-tune job failed: validate the training file before paying for another run

OpenAI fine-tune job failed: validate the training file before paying for another run

The symptom

The fine-tuning job errors out or sits in failed state. Each blind retry burns money, so never relaunch without knowing the cause.

Confirm the cause

Validate the training file locally before it touches the API:

1. JSONL shape. One complete JSON object per line. No pretty-printed multi-line objects, no trailing commas.
2. Message format. Each line needs a messages array, every message with role and content. A typo in a role name fails the whole file.
3. Upload purpose. The file must be uploaded with purpose fine-tune. A file uploaded for another purpose is not accepted as training data.
4. Size sanity. Check token counts per example. Pathological examples turn a validation problem into a billing problem.

Then read the job's events, not just its status. The events carry the specific validation error: which line, which field. Fix that exact thing.

The fix

1. Write a local validator: every line parses as JSON, has a messages array, every message has role and content, no empty strings where forbidden.
2. Fix all errors, re-upload with purpose fine-tune, launch a new job.
3. Start with a few hundred examples to prove the pipeline before training on the full dataset.
4. Estimate cost from token counts before the full run.

Verify the fix

The validator passes clean on the exact file uploaded. The new job reaches running state with steady progress in its events, not a validation error. Keep the validator in the pipeline so the next dataset is checked automatically.

OpenAI's fine-tuning guide requires the dataset uploaded formatted in JSONL with purpose fine-tune, and job events surface the specific validation errors. Local validation of line shape and message format before launching avoids paying for repeated failed jobs (https://platform.openai.com/docs/guides/fine-tuning).