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axolotl

Fine-tune LLMs with Axolotl using YAML configurations.

Updated Jul 3, 2026
Search Tags:#machine learning#fine-tuning#LLM#deep learning#YAML#axolotl
One-click install
npx skills add https://github.com/LynxLabVN/office-agent --skill axolotl-lynxlabvn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/LynxLabVN/office-agent --skill axolotl-lynxlabvn

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?

This Skill simplifies the fine-tuning of Large Language Models (LLMs) using Axolotl, offering a streamlined process for configuration, training, and debugging.

Core Features & Use Cases

  • LLM Fine-Tuning: Supports LoRA, DPO, GRPO, and other advanced fine-tuning techniques for LLMs.
  • Automated Development: Assists with axolotl development, leveraging official documentation and community best practices.
  • Quick Reference: Provides quick access to common patterns, configurations, and example code for efficient troubleshooting.
  • Documentation Access: Offers comprehensive documentation in references/ for in-depth information and detailed examples.
  • Customization: Allows for customization and extension through optional directories like scripts/, references/, and assets/.

Quick Start

To fine-tune an LLM using Axolotl, run the following command: axolotl train --config config.yaml

Frequently Asked Questions about axolotl

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

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

You can fine-tune an LLM using YAML by running the command `axolotl train --config config.yaml`, which parses your configurations to facilitate efficient model adaptation and training.

Can I use LoRA and DPO techniques for LLM fine-tuning?▼

Yes, LLM fine-tuning supports advanced techniques including LoRA, DPO, and GRPO, enabling efficient model adaptation and training using YAML configurations.

What dependencies do I need to set up for LLM training?▼

LLM training requires axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed to parse configurations and train models efficiently.

What is the best way to configure custom model training parameters?▼

Configuring custom model training parameters is best handled through YAML configurations, which allow streamlined customization and extension for fine-tuning Large Language Models.

Where can I find example code and documentation for troubleshooting LLM fine-tuning?▼

You can find example code and comprehensive documentation in the `references/` directory, providing quick access to common patterns and configurations for efficient troubleshooting.

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