axolotl

To configure Axolotl YAML for LoRA or QLoRA fine-tuning, you define your base model, target hardware, and dataset format within the YAML file to establish training parameters. The configuration aligns with documented API patterns for trainers and prompt strategies.

Setting up FSDP and context parallelism for multi-GPU LLM training involves applying concrete configuration patterns in your YAML file to distribute workloads. This approach addresses multi-GPU considerations and ensures proper context parallel sizing for distributed environments.

DPO, KTO, ORPO, and GRPO preference-based training workflows require specific dataset formats and prompt strategy concepts defined in your YAML configuration. Using the correct dataset formatting prevents common training errors and aligns with documented API references.

Axolotl fine-tuning applies to both single and distributed Linux and macOS environments. You can configure your YAML training runs to operate across these systems while avoiding common multi-GPU mistakes.

You can save compressed checkpoints during your fine-tuning run by configuring specific output parameters in your YAML file. This process generates smaller checkpoint files that enable faster downstream inference.

Multi-GPU LLM fine-tuning configurations often fail due to dataset-format mistakes or improper FSDP setup in the YAML file. Aligning your configuration with documented API references for datasets and trainers resolves these common errors.