axolotl

To configure YAML for LLM fine-tuning, you specify model choices and parameters within a configuration file to guide the axolotl workflow. This Skill provides expert assistance in generating and managing these YAML configurations for 100+ supported models.

Yes, you can run LoRA and DPO fine-tuning on macOS and Linux. This Skill supports LoRA/QLoRA and DPO/KTO/ORPO/GRPO methods, ensuring compatibility across these operating systems using required dependencies like torch and transformers.

The best way to fine-tune large language models using GRPO is by leveraging axolotl through YAML configurations. This approach streamlines the training workflow and supports advanced preference optimization techniques like GRPO.

Yes, you need DeepSpeed and PEFT for multimodal LLM fine-tuning using axolotl. These dependencies, alongside torch and accelerate, are required to execute the advanced fine-tuning workflows and manage memory efficiently.

Your axolotl fine-tuning workflow requires accelerate and transformers to handle model distribution and processing. These libraries are fundamental dependencies for managing the underlying large language model operations during training.