axolotl

You can configure LoRA and QLoRA fine-tuning for large language models by defining your parameters in a YAML file and using Axolotl's trainer utilities to execute the workflow. This approach integrates with PEFT and Transformers to streamline model adaptation.

Yes, Axolotl supports DPO, ORPO, GRPO, and KTO alignment training. You can implement these preference optimization techniques by specifying the desired method in your YAML configuration to guide the large language model alignment process.

To start multimodal fine-tuning, provide your model and goals to generate a YAML configuration. Axolotl uses PyTorch, Transformers, and accelerate to execute the workflow, allowing you to efficiently adapt models to specialized multimodal tasks.

The best way to scale LLM training is by integrating DeepSpeed and accelerate within your Axolotl YAML configuration. This combination optimizes distributed training resources, enabling efficient scaling for large language model fine-tuning workflows.

Yes, Axolotl relies on YAML-driven configuration to manage end-to-end fine-tuning workflows. This method allows you to define multi-model support, PEFT strategies, and trainer utilities in a structured format for efficient execution.

You should use QLoRA instead of full fine-tuning when adapting large language models under strict memory constraints. This parameter-efficient strategy modifies a subset of weights, reducing computational overhead while maintaining specialized task performance.