Hi 29556429,
Thanks for sharing the details. A 500 “system error” during fine‑tuning typically points to a service‑side or transient backend issue, rather than a problem with the training data or your configuration.
A few observations that may help:
We’ve seen similar behavior where fine‑tuning jobs fail with a 500 error due to temporary backend instability, capacity constraints, or internal validation issues in the fine‑tuning pipeline. When the same job succeeds after retrying, it usually confirms the issue isn’t data‑specific.
- Things to double‑check (even if they look fine)
- Dataset format and size comply with the documented fine‑tuning limits
- No recent changes to the model version or region being used
- Subscription / region isn’t hitting quota or capacity limits
- What you can do
- Retry the fine‑tuning job after some time (many of these errors are transient)
- If possible, try submitting the same job in a different region to rule out regional capacity issues
I Hope this helps. Do let me know if you have any further queries.
Thankyou!