Training & Optimization
Fine-tuning leverages a model’s pre-trained knowledge and adapts it to specific tasks, domains, or behaviors. This is far more efficient than training from scratch.
Process
- Start with a pre-trained base model
- Prepare task-specific training data
- Continue training with a lower learning rate
- Evaluate on validation data
- Deploy the fine-tuned model
Types
- Full Fine-Tuning: Update all model parameters
- Parameter-Efficient Fine-Tuning (PEFT): Update only a subset (LoRA, adapters)
- Instruction Fine-Tuning: Train on instruction-following data
- RLHF: Reinforce learning from human feedback
Benefits
- Faster than training from scratch
- Requires less data
- Improves task-specific performance
- Enables domain adaptation
Tags
training optimization technique
Related Terms
LoRA
Low-Rank Adaptation - a parameter-efficient fine-tuning method that updates only small low-rank matrices instead of full weights.
Pre-training
Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.
RLHF
Reinforcement Learning from Human Feedback - training models using human preferences to align behavior with human values.
Transfer Learning
Leveraging knowledge learned from one task/domain to improve performance on a related task with less data.