axolotl

Fine-tune large language models with YAML configurations using LoRA, DPO, and GRPO.

Updated Jun 19, 2026

Search Tags:#llm #fine-tuning #yaml #lora #dpo #grpo #axolotl

Ready to run

npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill axolotl-anandaanugrahhandyanto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/AnandaAnugrahHandyanto/savarez_agent/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill axolotl-anandaanugrahhandyanto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl simplifies fine-tuning large language models with YAML-driven configurations, enabling reproducible experiments and easy experimentation with LoRA, DPO, and GRPO.

Core Features & Use Cases

YAML-based fine-tuning workflows for 100+ models with LoRA/QLoRA, DPO, KTO, ORPO, and GRPO, including multimodal support.
Centralized documentation and guidance for Axolotl development and best practices across RLHF, GRPO, and LLM fine-tuning.
Real-world scenario: accelerate rapid iteration of model adapters across multiple projects by reusing a single YAML config.

Quick Start

Create a YAML config to fine-tune an LLM with LoRA/DPO/GRPO using Axolotl and start training.

Frequently Asked Questions about axolotl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fine-tune large language models using YAML configurations?▼

Axolotl enables YAML-driven LLM fine-tuning by allowing you to create a configuration file specifying parameters for LoRA, DPO, or GRPO training across over 100 models, simplifying reproducible experiments and rapid iteration.

What is the best way to manage complex RLHF-style fine-tuning workflows?▼

Managing complex RLHF-style fine-tuning workflows is done through Axolotl, which provides centralized guidance and supports DPO, KTO, ORPO, and GRPO techniques to streamline training across multiple projects.

Can I reuse a single YAML config to accelerate model adapter iteration across projects?▼

Yes, you can reuse a single YAML config across multiple projects to accelerate rapid iteration of model adapters, enabling ML engineers to maintain consistent training parameters while experimenting with different models.

Does YAML-based LLM fine-tuning support multimodal data?▼

YAML-based LLM fine-tuning supports multimodal data through Axolotl, enabling ML engineers and researchers to train models across diverse data types beyond standard text inputs.

What fine-tuning techniques are available for LLM training beyond standard LoRA?▼

Beyond standard LoRA, Axolotl supports QLoRA, DPO, KTO, ORPO, and GRPO fine-tuning techniques, providing ML engineers with parameter-efficient tuning and complex reinforcement learning options for over 100 models.

Do I need Axolotl for rapid experimentation with LoRA and DPO?▼

You need Axolotl when seeking YAML-driven configurations for reproducible experiments with LoRA and DPO, particularly when managing complex workflows across multiple models and requiring rapid adapter iteration.