LoRA (Low-Rank Adaptation)
technique technique
Efficient fine-tuning with low-rank matrices
Supported languages
LoRA is a revolutionary fine-tuning technique that allows adapting large language models without training all their parameters. It works by freezing the original model weights and injecting trainable low-rank matrices into each transformer layer, drastically reducing memory requirements and training time.
Concepts
low-rank-decompositionadaptersparameter-efficient-fine-tuningmatrix-factorizationfrozen-weightstrainable-parametersrank-selection
Pros and Cons
Ventajas
- + Reduces memory usage up to 10x compared to full fine-tuning
- + Significantly faster training
- + Enables fine-tuning on consumer hardware (8-16GB GPUs)
- + Small adapter models easy to share (MB vs GB)
- + Multiple adapters can be loaded dynamically
- + Preserves original model knowledge
Desventajas
- - May not capture very complex changes in model behavior
- - Requires careful hyperparameter selection (rank, alpha)
- - Quality may be slightly lower than full fine-tuning in extreme cases
- - Not all frameworks support it equally well
Casos de Uso
- Adapting LLMs to specific domains (legal, medical, technical)
- Fine-tuning with limited computational resources
- Creating multiple specialized versions of a base model
- Model personalization for specific tasks
- Rapid experimentation with different configurations