Fine Tuning vs Parameter Efficient Fine Tuning
Fine tuning and Parameter Efficient Fine Tuning are two methods used to adapt large models to new tasks. Both change model behavior, but they use different training strategies.
What Is Fine Tuning
Fine tuning updates all model weights. You start from a pretrained model and train it on a new dataset. This creates a strong task specific model.
How It Works
- Load a pretrained model.
- Train on task data.
- Update all parameters.
- Save the full model.
Strengths
- High performance
- Full control over the model
- Strong adaptation to the new domain
Limitations
- High compute cost
- Large memory needs
- Slow training
What Is Parameter Efficient Fine Tuning
Parameter Efficient Fine Tuning updates only a small part of the model. The rest stays frozen. This reduces training cost and memory needs.
Common PEFT Methods
- LoRA
- Adapters
- Prefix tuning
- Prompt tuning
How PEFT Works
- Freeze the main model.
- Add small trainable modules.
- Train only these small modules.
- Keep the core weights untouched.
Strengths
- Low compute
- Fast training
- Small storage size
Limitations
- Lower flexibility than full fine tuning
- Task performance can depend on tuning type
Main Differences
| Aspect | Fine Tuning | PEFT |
|---|---|---|
| Updated Parameters | All weights | Small modules |
| Compute Cost | High | Low |
| Memory Need | Large | Small |
| Flexibility | High | Medium |
| Use Case | Large training budgets | Lightweight adaptation |
When To Use Each Method
Use Fine Tuning When
- You have strong compute resources
- You need full control
- You target maximum accuracy
Use PEFT When
- You have limited compute
- You want fast experimentation
- You need small and portable models
Fine Tuning vs PEFT in Moroccan Darija
Fine tuning kayupdate l model kaml. PEFT kayupdate ghir parte sghira men l model. L hadaf howa nkhdmo b cost w memory sghirin.
Fine Tuning
- Kayupdate kolchi.
- Performance qaouiya.
- Cost kbir.
PEFT
- Kayfreeze l model.
- Kayzid modules sgharin.
- Training sahl w rapide.
Conclusion
Fine tuning changes all weights for maximum control. Parameter Efficient Fine Tuning updates small modules to save compute. Both methods help you adapt models to new tasks with clear benefits.