axolotl

Configure YAML-based Axolotl pipelines for LLM fine-tuning with RLHF and LoRA/QLoRA.

1.0k|117|Updated Feb 26, 2026

Search Tags:#llm #fine-tuning #training #datasets #rlhf #axolotl

Ready to run

npx skills add https://github.com/OpenLAIR/dr-claw --skill axolotl-openlair

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/fine-tuning/axolotl
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill axolotl-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl provides a comprehensive framework for end-to-end fine-tuning of large language models, including support for RLHF, LoRA/QLoRA optimization, and PEFT.

Core Features & Use Cases

End-to-end fine-tuning pipelines for LLMs, including RLHF and LoRA/QLoRA adapters.
Support for multiple training regimes (pretraining, SFT, RLHF/PRM) and diverse datasets via streaming and prepared data.
Patching and optimization utilities to accelerate training, including monkey-patching for LoRA and fast attention.

Quick Start

Configure and run a full Axolotl fine-tuning workflow to start training a model with your dataset.

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I configure YAML-based pipelines for LLM fine-tuning?▼

YAML-based pipelines for LLM fine-tuning are configured by defining training regimes, custom datasets, and adapter settings within a YAML file, enabling reproducible end-to-end model training workflows.

Can I use LoRA and QLoRA adapters for efficient LLM training?▼

Yes, LoRA and QLoRA adapters are fully supported for efficient LLM fine-tuning, complete with optimization and monkey-patching utilities to accelerate training.

What is the best way to handle diverse datasets for RLHF training?▼

Handling diverse datasets for RLHF training is streamlined through support for both streaming and prepared data pipelines, effectively accommodating pretraining, SFT, and post-training PRM workflows.

Does this fine-tuning framework support end-to-end RLHF workflows?▼

Yes, the framework supports end-to-end RLHF workflows, targeting researchers and engineers who need configurable data pipelines and patching utilities for advanced post-training reinforcement learning.

Do I need patching utilities to accelerate large language model training?▼

Patching utilities like monkey-patching for LoRA and fast attention are integrated to accelerate large language model training and optimize the fine-tuning process without requiring external modifications.

When should I use QLoRA over standard fine-tuning for large language models?▼

QLoRA should be used over standard fine-tuning when optimizing memory efficiency for large language models, leveraging PEFT and adapter integrations to maintain reproducible training workflows.