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axolotl

Configure and execute YAML-driven LLM fine-tuning workflows with Axolotl.

31|3|Updated May 7, 2026
Search Tags:#llm#fine-tuning#yaml#lora#dpo#grpo#axolotl
One-click install
npx skills add https://github.com/markwang2658/hermes-windows-native --skill axolotl-markwang2658
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/markwang2658/hermes-windows-native/tree/main/hermes-agent/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/markwang2658/hermes-windows-native --skill axolotl-markwang2658

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl streamlines the complexity of configuring and running YAML-driven fine-tuning workflows for large language models, enabling reproducible experiments with minimal boilerplate.

Core Features & Use Cases

  • YAML-based configuration for LoRA, DPO, GRPO fine-tuning workflows.
  • Supports multiple RLHF and SFT scenarios with shared environments.
  • Real-world use: rapidly prototype fine-tuning on a new model or dataset without heavy scripting.

Quick Start

Provide a YAML configuration to kick off a LoRA, DPO, or GRPO fine-tuning job on your chosen model.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I configure LLM fine-tuning workflows without writing heavy Python scripts?▼

You can configure LLM fine-tuning workflows by defining parameters in a YAML file, which allows you to execute LoRA, DPO, or GRPO training jobs on your chosen model with minimal boilerplate.

What is YAML-driven fine-tuning and how does it handle different training methods like LoRA and DPO?▼

YAML-driven fine-tuning uses configuration files to define training parameters for methods like LoRA, DPO, and GRPO, enabling reproducible SFT and RLHF experiments across diverse datasets and trainer backends.

Do I need specific dependencies installed to run GRPO and DPO fine-tuning jobs?▼

Yes, running GRPO and DPO fine-tuning jobs expects dependencies such as axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed to be installed in your environment.

Can I use YAML configuration for both pretraining-like and RLHF-style workflows?▼

Yes, YAML configuration supports both pretraining-like and RLHF-style workflows, allowing you to rapidly prototype fine-tuning on new models or datasets using shared environments and multiple trainer backends.

Where can I find troubleshooting references for YAML-driven LLM fine-tuning?▼

Troubleshooting references for YAML-driven LLM fine-tuning are exposed under the references and assets directories, providing materials to resolve issues with your LoRA, DPO, or GRPO workflows.

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