LoRA
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
/ quick answer
LoRA freezes the base model and trains low-rank update matrices (a few % of parameters). Enables per-customer or per-task fine-tunes for cents instead of dollars. Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
/ frequently asked
What is LoRA?
LoRA freezes the base model and trains low-rank update matrices (a few % of parameters). Enables per-customer or per-task fine-tunes for cents instead of dollars.
What is an example of LoRA?
A 7B model gets 20 LoRA adapters, one per customer, all loaded dynamically.
Why does LoRA matter?
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
What should I learn after LoRA?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
- →RLHF
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
- →DPO (Direct Preference Optimization)
A simpler alternative to RLHF that skips the reward model.
- →Model Distillation
Training a small model to mimic a large one.
Related workflows
Turn this into a repeatable process.
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Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
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