Fine-tune large language models with YAML-driven LoRA, DPO, and GRPO configurations.
npx skills add https://github.com/x-TheFox/Corvus --skill axolotl-x-thefox
Please help me install this Agent Skill. Skill: axolotl Source: https://github.com/x-TheFox/Corvus/tree/main/skills/mlops/training/axolotl Command: npx skills add https://github.com/x-TheFox/Corvus --skill axolotl-x-thefox
Axolotl provides a declarative YAML-based workflow to fine-tune large language models using advanced techniques like LoRA, DPO, GRPO, KTO, and ORPO, simplifying complex experiment setup and reproducibility.
Create a YAML config that specifies base_model, fine-tuning method, and adapters, then run the Axolotl trainer to begin fine-tuning.
High-intent search queries and answers about installing and using this skill.
YAML-driven fine-tuning configures large language models by specifying base models, adapters, and methods like LoRA and DPO in a declarative file. This approach simplifies complex experiment setup and ensures reproducibility across multi-GPU environments.
GRPO is an advanced reinforcement learning technique used for fine-tuning large language models. You need GRPO when aligning model outputs with specific reward signals, which can be orchestrated alongside LoRA and DPO using declarative YAML configurations.
Distributed training across multi-GPU environments is fully supported for LLM fine-tuning. The workflow handles end-to-end training orchestration and rapid prototyping across GPU clusters using YAML-driven configurations.
You need the Axolotl package installed along with compatible versions of PyTorch, Transformers, Datasets, and PEFT. These dependencies must be available in your environment to run the YAML-driven trainer for large language models.
The best way to set up multimodal and RLHF workflows is through modular prompt strategies and dataset formats. YAML configurations enable these complex workflows, allowing rapid prototyping and end-to-end training orchestration.
Declarative YAML configs simplify complex experiment setup and ensure reproducibility for LLM fine-tuning. By defining base models, methods, and adapters in YAML, you streamline experiment replay and orchestration across distributed training environments.
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