axolotl

Name: axolotl
Availability: InStock
Author: tangzheng202202

Guide fine-tuning LLMs with Axolotl using YAML configs and LoRA/QLoRA.

1|Updated Apr 13, 2026

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

Ready to run

npx skills add https://github.com/tangzheng202202/hermes-skills --skill axolotl-tangzheng202202

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/tangzheng202202/hermes-skills/tree/main/03-mlops/mlops/training/axolotl
Command: npx skills add https://github.com/tangzheng202202/hermes-skills --skill axolotl-tangzheng202202

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires axolotl, torch, transformers, datasets, peft, accelerate, deepspeed, and includes references (resource) components.

What problem does it solve?

Axolotl provides expert guidance for fine-tuning LLMs using YAML configs, including support for LoRA/QLoRA, DPO/ORPO/GRPO RLHF workflows, and multimodal data, helping teams accelerate model customization.

Core Features & Use Cases

Comprehensive guidance for end-to-end Axolotl-based training, evaluation, and deployment workflows.
Support for LoRA/QLoRA optimizations, multiple RLHF strategies (DPO/ORPO/GRPO), and multimodal data pipelines.
Access to official references, example patterns, and practical tutorials to accelerate development.

Quick Start

Install Axolotl, create a config YAML for your model, and start training 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 fine-tune LLMs with YAML configs and LoRA?▼

Fine-tune LLMs with YAML configs by installing Axolotl, creating a config file for your model, and starting training with your dataset. This skill provides official references and patterns for LoRA/QLoRA optimizations to accelerate model customization.

Can I use DPO or GRPO for RLHF workflows?▼

Yes, DPO and GRPO are supported for RLHF workflows. This skill provides expert guidance for integrating these multiple RLHF strategies, alongside ORPO, into your end-to-end training workflows using Axolotl.

What's the best way to fine-tune LLMs for multimodal data?▼

Fine-tune LLMs for multimodal data using Axolotl's supported multimodal data pipelines. This skill offers comprehensive guidance and practical tutorials to help you build end-to-end training and evaluation workflows for multimodal models.

Does this skill work with DeepSpeed and accelerate?▼

Yes, this skill works with DeepSpeed and accelerate, which are listed dependencies. It provides expert guidance for Axolotl-based training workflows that utilize these frameworks for distributed training and hardware acceleration.

Why use QLoRA over LoRA for model customization?▼

Use QLoRA over LoRA when you need optimized memory efficiency for model customization. This skill provides official references and example patterns for both LoRA and QLoRA optimizations to help you accelerate development.

Do I need PyTorch and transformers to start training?▼

Yes, PyTorch and transformers are required dependencies to start training. You need these foundational libraries installed alongside Axolotl to execute the YAML-configured training workflows and model evaluation.