axolotl

To configure LoRA fine-tuning with Axolotl, you need to set up a YAML configuration file specifying the correct training options, dataset schema, and target method. This Skill guides you through selecting the appropriate parameters for a reproducible local or cloud training run.

Axolotl supports pretraining, instruction, conversation, template-free, preference, reward, and multimodal dataset formats. You must choose the correct dataset schema that matches your selected training method to ensure proper model adaptation.

Yes, Axolotl supports QLoRA for quantized training and DPO for preference alignment. This Skill helps you configure the appropriate YAML options and select the correct training method among LoRA, QLoRA, DPO, KTO, ORPO, or GRPO.

Troubleshooting an Axolotl training run involves validating your YAML configuration, ensuring your dataset format matches the required schema, and verifying the selected training method. This Skill provides guidance to identify and resolve setup issues across local or cloud workflows.

Yes, Axolotl supports multimodal scenarios for both fine-tuning and inference. You can use this Skill to configure workflows that handle multimodal dataset formats and set up the necessary training or evaluation parameters.

You should use RLHF methods like KTO, ORPO, or GRPO when aligning model outputs to human preferences, whereas standard LoRA or QLoRA is for general task adaptation. This Skill guides you in choosing and configuring the appropriate method for your specific goal.