Fine-Tuning Failure
Fine-tuning failures occur when pretrained model knowledge is destroyed by aggressive learning rates or insufficient data.
Fine-tuning failures occur when pretrained model knowledge is destroyed by aggressive learning rates or insufficient data.
What this failure is
Fine-Tuning Failure is a Training Stability failure seen during ML training runs. Fine-tuning failures occur when pretrained model knowledge is destroyed by aggressive learning rates or insufficient data. Common tags: Fine Tuning, Catastrophic Forgetting, Lora, Pretrained.
Is this what broke your run? Paste your log.
You're reading about Fine-Tuning Failure. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
Learning rate too high (destroys pretrained features). Too many epochs overfit to small dataset. LoRA not used: full fine-tuning expensive. No replay of original data. Wrong task head: classification vs regression mismatch. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Fine-tuned model is worse than pretrained
- Fine-tuning causes catastrophic forgetting
- Model loses general capabilities after fine-tuning
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Validation accuracy is lower than pretrained zero-shot | Learning rate too high (destroys pretrained features) |
| Model loses language/general capabilities | Too many epochs overfit to small dataset |
| Catastrophic forgetting on original task | LoRA not used: full fine-tuning expensive |
Which systems are affected
- Fine-tuning LLMs for specific tasks
- Fine-tuning vision models
- Domain adaptation
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Validation accuracy is lower than pretrained zero-shot
- ✓Verified signal present: Model loses language/general capabilities
- ✓Verified signal present: Catastrophic forgetting on original task
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card. Daily allowance follows verified trust tier. Instant access.
Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionRelated failures to investigate next
Root cause
- Learning rate too high (destroys pretrained features)
- Too many epochs overfit to small dataset
- LoRA not used: full fine-tuning expensive
- No replay of original data
- Wrong task head: classification vs regression mismatch
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.