LoRA from Scratch — Parameter-Efficient Fine-Tuning

What you'll be able to do

✓Build a LoRA adapter from scratch — freeze a pretrained weight and learn a low-rank update in its place
✓Inject LoRA into the attention of a real GPT-2 and train only ~0.2% of its parameters
✓Match full fine-tuning accuracy at a fraction of the trainable parameters, and save an adapter that is kilobytes, not megabytes
✓Merge an adapter back into the base weights for zero-overhead inference, and swap adapters at serve time
✓Explain QLoRA — quantizing the frozen base so a huge model fine-tunes on a single GPU

Build the technique behind almost every fine-tuned open LLM you have used — freeze the base, train a tiny low-rank adapter, and ship a model that matches full fine-tuning while training a fraction of a percent of the weights.

⌁ LoRA is how the industry adapts LLMs — Hugging Face's PEFT library, Microsoft (who invented it), Databricks, and serving platforms like Predibase run one frozen base model with many swappable adapters.

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