Custom Loss Functions And Training Objectives

Streamlined LLM fine-tuning — YAML config, LoRA/QLoRA, multi-GPU, data preprocessing.

Unique: Axolotl provides built-in DPO support without requiring separate implementations, with configuration-driven objective selection and automatic token masking. Custom loss registration allows extending training objectives without forking the framework.

vs others: More accessible DPO implementation than manual PyTorch code, with built-in support for multiple objectives that eliminates writing separate training loops.