fine▌

TRL provides post-training methods for aligning language models with human preferences.

Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods.

Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization. \n \n Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment \n Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments \n Includes five-stage workflow: dataset preparation, method selection, training wit