axolotl

Configuring LLM fine-tuning involves building a YAML file that defines dataset formats, trainer options, and LoRA optimization parameters. Validating these settings ensures correct preprocessing, mixed precision, and distributed training setup before launching the workflow.

DPO, GRPO, KTO, and ORPO are supported preference optimization techniques for LLM post-training. These methods require specific YAML configurations and properly formatted preference datasets to align models during training.

Multimodal LLM training requires datasets formatted to Axolotl's specific standards for combined text and image inputs. Proper preprocessing and YAML configuration ensure the trainer correctly interprets multimodal data during fine-tuning.

Model quantization and adapter merging are supported post-training operations. After fine-tuning, you can merge LoRA adapters into the base model and apply quantization to reduce model size, guided by YAML configurations and official API references.

LLM fine-tuning workflows often fail when custom instruction datasets do not match Axolotl's required schema. Troubleshooting involves validating the YAML configuration, checking dataset preprocessing, and verifying trainer options against official API documentation.

YAML literacy is required to configure and troubleshoot LLM training, evaluation, and inference workflows. Understanding Axolotl dataset formats, trainer options, and API references is necessary to build valid configs and resolve debugging scenarios.