axolotl

Configure Axolotl YAML files for LLM fine-tuning with LoRA, DPO, and GRPO.

Updated Jul 3, 2026

Search Tags:#llm #fine-tuning #lora #dpo #grpo #axolotl

Ready to run

npx skills add https://github.com/Toqsick/MaxClaw --skill axolotl-toqsick

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/Toqsick/MaxClaw/tree/main/.claude/skills/axolotl
Command: npx skills add https://github.com/Toqsick/MaxClaw --skill axolotl-toqsick

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides expert guidance for fine-tuning LLMs with Axolotl, offering comprehensive support for YAML LLM fine-tuning using LoRA, DPO, GRPO, and multimodal support.

Core Features & Use Cases

LLM Fine-Tuning: Offers comprehensive assistance with axolotl development, including YAML config management, model training with LoRA, DPO, GRPO, and multimodal support.
Debugging and Best Practices: Provides debugging support and best practices for axolotl.
Use Case: Ideal for data scientists, ML engineers, and AI researchers working on LLM fine-tuning and require a detailed understanding of Axolotl features and APIs.

Quick Start

Run the axolotl skill with the command: axolotl

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I fine-tune an LLM using YAML configurations with Axolotl?▼

You fine-tune LLMs with Axolotl by defining training parameters, datasets, and model paths within a YAML configuration file, which the framework parses to orchestrate the training pipeline.

Can I use Axolotl for DPO and GRPO training, or is it limited to LoRA?▼

Axolotl supports multiple alignment and fine-tuning techniques including LoRA, DPO, and GRPO, allowing you to choose the appropriate method for your specific reinforcement learning or preference optimization needs.

What dependencies do I need to set up before starting LLM fine-tuning with Axolotl?▼

Before fine-tuning with Axolotl, you need a Python environment with torch, transformers, datasets, peft, accelerate, and deepspeed installed to handle model processing and distributed training workloads.

Does Axolotl support multimodal LLM fine-tuning, or is it strictly text-based?▼

Axolotl provides multimodal support for fine-tuning LLMs, enabling you to train models on datasets that include image and text inputs alongside traditional text-only training workflows.

Why should I choose Axolotl over other frameworks for LLM fine-tuning?▼

Axolotl streamlines LLM fine-tuning by unifying LoRA, DPO, GRPO, and multimodal training under a single YAML-driven interface, reducing boilerplate while integrating DeepSpeed and PEFT for scalable performance.