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axolotl

Fine-tune LLMs using YAML configurations with LoRA, DPO, and GRPO techniques.

2|Updated May 22, 2026
Search Tags:#fine-tuning#LLM#YAML#LoRA#GRPO#DPO
One-click install
npx skills add https://github.com/519lab/thoth-agent --skill axolotl-519lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/519lab/thoth-agent/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/519lab/thoth-agent --skill axolotl-519lab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires axolotl, torch, transformers, datasets, peft, accelerate, deepspeed, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The axolotl Skill unit streamlines the process of fine-tuning large language models (LLMs) using YAML configurations, making the task more accessible and efficient.

Core Features & Use Cases

  • YAML Configuration Support: Utilize YAML for defining LLM fine-tuning parameters, including LoRA, DPO, GRPO, and multimodal support.
  • Comprehensive Documentation: Offers 100+ models, configurations, and guidance for LoRA, QLoRA, DPO, KTO, ORPO, GRPO, and YAML-based fine-tuning.
  • Use Case: Perfect for developers and researchers looking to fine-tune LLMs with various techniques like LoRA and DPO for improved performance.

Quick Start

Use the axolotl skill to fine-tune the Llama model with LoRA on the provided dataset.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I fine-tune a LLM using YAML configurations?▼

Fine-tune a LLM using YAML configurations by defining parameters for techniques like LoRA, DPO, and GRPO. This framework streamlines the process, making it accessible without writing extensive boilerplate training code.

Can I use DeepSpeed and accelerate for LLM fine-tuning?▼

Yes, you can use DeepSpeed and accelerate for LLM fine-tuning. The framework requires both libraries alongside torch, transformers, datasets, and peft to optimize and distribute training workloads efficiently.

What is the best way to apply LoRA and DPO techniques to large language models?▼

The best way to apply LoRA and DPO techniques is using a configuration-driven framework that supports YAML. It provides comprehensive documentation and 100+ model configurations to guide you through the training process.

Does this framework support GRPO and multimodal LLM fine-tuning?▼

Yes, this framework supports GRPO and multimodal LLM fine-tuning. You can define these parameters within your YAML configuration file to train models across various advanced techniques.

What dependencies do I need to set up before starting LLM fine-tuning?▼

Before starting LLM fine-tuning, you need to install axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed. These libraries provide the foundational environment for model training and distributed processing.

Why use YAML for defining LLM fine-tuning parameters instead of Python scripts?▼

Using YAML for LLM fine-tuning parameters separates configuration from code, making the process more accessible and efficient. It allows you to quickly adjust training settings for techniques like QLoRA, KTO, and ORPO without modifying scripts.

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