⚙️ AI Hardware

LoRA Hyperparameters: The Silent Fine-Tuning Killers

LoRA concentrates gradients like a laser—burn too hot, and your model craters. Here's the no-guesswork guide to hyperparameters that actually work.

Marcus Rivera 📅 Mar 23, 2026 ⏱️ 3 min read 👁️ 16 views
Visualization of LoRA gradient concentration versus full fine-tuning

⚡ Key Takeaways

  • LoRA demands 2e-4 LR max due to gradient concentration on 1% params.
  • Alpha/rank ratio controls injection strength—set equal for stability.
  • Warmup schedulers prevent early divergence; overfit hits in epoch 1.

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Marcus Rivera
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Marcus Rivera

Tech journalist covering AI business and enterprise adoption. 10 years in B2B media.

#LoRA fine-tuning #hyperparameters #learning rate #overfitting

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Originally reported by Towards AI

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