Supervised Fine-Tuning of Large Language Models
Supervised Fine-Tuning of Large Language Models
Pre-trained models are general-purpose. Fine-tuning adapts them for specialized tasks.
1) What is Supervised Fine-Tuning?
Supervised Fine-Tuning (SFT) involves training a pre-trained model on labeled input-output pairs specific to your domain.
2) When to Use SFT
- Domain-specific terminology
- Structured output requirement
- Consistent tone and behavior
3) Training Workflow
- Prepare dataset
- Load base model
- Train on domain data
- Evaluate performance
4) Limitations
- High compute requirement
- Risk of overfitting
- Model drift
5) Summary
SFT improves specialization but must be carefully evaluated before deployment.
