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axolotl

Document Axolotl framework configurations and techniques for LLM fine-tuning.

Updated Aug 23, 2026
Search Tags:#llm#fine-tuning#yaml#lora#huggingface#dpo#axolotl
One-click install
npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill axolotl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/AXGZ21/hermes-agent-railway/tree/main/skills/mlops/axolotl
Command: npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill axolotl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert guidance and comprehensive documentation for fine-tuning Large Language Models (LLMs) using the Axolotl framework, simplifying complex LLM customization.

Core Features & Use Cases

  • Axolotl Expertise: Offers detailed information on Axolotl features, APIs, and best practices.
  • Fine-tuning Guidance: Assists with configurations for LoRA/QLoRA, DPO/KTO/ORPO/GRPO, and multimodal support.
  • Use Case: A user wants to fine-tune a Llama 3 model for a specific task. They can use this Skill to understand Axolotl's YAML configuration for LoRA, data formatting, and training parameters, ensuring a successful fine-tuning process.

Quick Start

Use the axolotl skill to find examples of FSDP configuration in YAML.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I configure YAML for fine-tuning a Llama 3 model with LoRA?▼

To configure YAML for fine-tuning a Llama 3 model with LoRA, you need to define specific training parameters, data formatting, and model architectures within your Axolotl configuration file to ensure a successful customization process.

What is the difference between DPO, KTO, ORPO, and GRPO for LLM fine-tuning?▼

DPO, KTO, ORPO, and GRPO are distinct fine-tuning techniques for Large Language Models. The Axolotl framework provides specific configurations and expert guidance to implement these preference tuning methods effectively.

Can I use Axolotl for multimodal LLM fine-tuning?▼

Yes, you can use Axolotl for multimodal LLM fine-tuning. The framework provides dedicated configurations and support to customize models that process multiple data types alongside text.

How do I set up FSDP configuration in YAML for LLM training?▼

To set up FSDP configuration in YAML for LLM training, you can reference practical examples provided by the Axolotl framework, which detail the necessary parameters for distributed training strategies.

Does Axolotl support QLoRA for efficient LLM customization?▼

Yes, Axolotl supports QLoRA for efficient LLM customization. You can configure QLoRA parameters within your YAML setup to reduce memory usage while fine-tuning Large Language Models.

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