LoRA (Low-Rank Adaptation)
LoRA is an efficient fine-tuning technique that allows developers to adapt large models for specific tasks (like function-calling) without retraining all the model's parameters.
How it Works
- Frozen Base: The billions of weights in the base model are "frozen" and never updated.
- Trainable Adapters: Small pairs of rank decomposition matrices are inserted into the model's layers.
- Reduced Overhead: Only these small adapters are trained, reducing the number of trainable parameters by up to 10,000x.
Benefits for Agents
- Speed: Training is significantly faster.
- Memory Efficiency: Models can be fine-tuned on consumer-grade hardware.
- Modularity: You can create small "Agent Adapters" (a few hundred MBs) that can be swapped or merged with different base models.