Documentation
README
PEFT (Parameter-Efficient Fine-Tuning)
Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.
When to use PEFT
Use PEFT/LoRA when:
- Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100)
- Need to train <1% parameters (6MB adapters vs 14GB full model)
- Want fast iteration with multiple task-specific adapters
- Deploying multiple fine-tuned variants from one base model
Use QLoRA (PEFT + quantization) when:
- Fine-tuning 70B models on single 24GB GPU
- Memory is the primary constraint
- Can accept ~5% quality trade-off vs full fine-tuning
Use full fine-tuning instead when:
- Training small models (<1B parameters)
- Need maximum quality and have compute budget
- Significant domain shift requires updating all weights
This is the opening of the README. Read the full README on GitHub.