📝 Hugging Face TRL + PEFT
Description
- TRL + PEFT is currently the safest general-purpose stack for LLM fine-tuning and post-training: its components have clear responsibilities, work together officially, and can scale from a single-GPU adapter run to distributed training.
| Component | Responsibility |
|---|---|
| TRL | Training loops for SFT, DPO, GRPO, KTO, distillation, and other post-training methods |
| PEFT | LoRA, QLoRA, and other parameter-efficient adapter implementations |
| Transformers | Pretrained models, configurations, and tokenizers |
| Accelerate / DeepSpeed | Multi-GPU and multi-node execution, sharding, mixed precision, and memory optimization |
| Unsloth | Optional optimized fine-tuning path for supported models and hardware |
Key points
- TRL officially integrates with Transformers, PEFT, Accelerate, DeepSpeed, and Unsloth.
- Use TRL to choose the post-training objective and training loop, then use PEFT when only adapter parameters should be updated.
- Keep Transformers as the model/tokenizer layer and add Accelerate or DeepSpeed only when the run needs distributed or memory-efficient execution.