A student fine-tuned a model hard on customer-support replies and it got noticeably worse at basic reasoning questions it had handled fine before. That's catastrophic forgetting — narrow, aggressive fine-tuning overwrites general capability the base model had.
Why it happens
Too many epochs on too narrow a dataset pushes the model's weights hard in one direction. LoRA reduces this risk versus full fine-tuning because it touches far fewer parameters, but it isn't immune, especially at high rank and learning rate.
How to prevent it
- Evaluate on a general-capability set before and after fine-tuning, not just on the target task.
- Fewer epochs and a lower learning rate first; add more only if the target task genuinely needs it.
- Mix a small slice of general instruction data into the fine-tuning set alongside the task-specific data.
See reading a loss curve during fine-tuning for how forgetting often shows up as a training loss that looks fine while eval quality quietly drops elsewhere.
About Pranjul Rathour

Taking questions during a session

At an Integral Startup Foundation hackathon

In a packed college auditorium
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
- Email: [email protected]
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- GitHub: https://github.com/Pranjulrathour
Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: [email protected] · Invite me to your campus
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