TRL Fine Tuning

What problem does it solve?

Fine-tune large language models using reinforcement learning with TRL to align behavior with human preferences.

Core Features & Use Cases

• SFT for instruction tuning and task-specific fine-tuning
• DPO, PPO/GRPO workflows for preference alignment and RL-based optimization
• Reward modeling and evaluation, plus LoRA/PEFT for memory-efficient training
• Seamless integration with HuggingFace Transformers and common datasets for easy experimentation

Quick Start

Run a TRL-based reinforcement learning fine-tuning workflow on your base model using instruction- or preference-data.