axolotl

Automate end-to-end RLHF training workflows with YAML-config-driven Axolotl orchestration.

Updated Apr 10, 2026

Search Tags:#mlops #fine-tuning #lora #qlora #rlhf #axolotl

Ready to run

npx skills add https://github.com/overviewlabs/WHOX --skill axolotl-overviewlabs

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl provides a structured framework and reference implementations for building, training, and iterating RLHF- and LLM-fine-tuning pipelines, enabling consistent experimentation and deployment.

Core Features & Use Cases

End-to-end RLHF training support with DPO, ORPO, GRPO, and GRPO; LoRA/QLoRA acceleration; support for SFT and reward modeling workflows.
YAML-config driven orchestration for data loading, preprocessing, model loading, training loops, evaluation, and deployment, with mixed precision and multi-GPU support.
Real-world use cases include fast prototyping of RLHF experiments, reproducible training pipelines on single GPUs or large clusters, and multimodal fine-tuning patterns.

Quick Start

Install Axolotl, prepare a YAML config describing your model and task, and start training with the axolotl CLI.

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I automate RLHF fine-tuning workflows end-to-end?▼

Automating RLHF fine-tuning workflows involves orchestrating data preparation, model loading, and iterative training. Using a configuration-driven framework like Axolotl streamlines this by applying YAML configs to manage supervised fine-tuning, reward modeling, and DPO or ORPO across single or multi-GPU clusters.

Can I use LoRA and QLoRA for mixed precision LLM training on a single GPU?▼

Yes, LoRA and QLoRA acceleration support mixed precision LLM training on a single GPU. This approach reduces memory consumption and enables efficient fine-tuning while maintaining reproducible training pipelines and supporting multimodal capabilities.

What is the best way to configure LLM training pipelines without writing boilerplate code?▼

The best way to configure LLM training pipelines without boilerplate is using YAML-config driven orchestration. This method manages data loading, preprocessing, model loading, and training loops by defining requirements for adapters, chat templates, and datasets directly in configuration files.

Does Axolotl support multimodal fine-tuning and multi-GPU clusters?▼

Yes, Axolotl supports multimodal fine-tuning patterns and scales from single-GPU environments to large multi-GPU clusters. It integrates with PyTorch and transformers to handle mixed precision training and complex RLHF workflows across distributed hardware.

When do I need YAML configuration for supervised fine-tuning and reward modeling?▼

You need YAML configuration for supervised fine-tuning and reward modeling when you require reproducible training pipelines and consistent experimentation. This config-driven approach satisfies requirements for chat templates, dataset loading, and adapter integration without manual code adjustments.