Applied LLMs
LoRA Training Pitfalls
LoRA's low-rank approximation introduces subtle failure modes around rank selection, learning rate asymmetry, and target-module coverage that can silently degrade fine-tuned model quality.
intermediate · 7 min read · Premium
You spend three hours fine-tuning a 7B model with LoRA, eval loss curves look healthy, and then the model hallucinates on every domain-specific prompt you care about. The loss was fine. The checkpoint was wrong. This is not unusual. LoRA's elegance as a training method hides a handful of failure modes that are easy to miss precisely because they don't surface as obvious training crashes.
This concept unpacks the most consequential pitfalls: rank misconfiguration, the hidden learning-rate asymmetry between adapter matrices, wrong target modules, quantisation-induced initialisation errors, and catastrophic forgetting vs. under-adaptation. Knowing where each one hides lets you debug faster and design better fine-tuning runs.
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