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axolotl

Fine-tune large language models with Axolotl using LoRA, DPO, GRPO, and multimodal training.

Updated Jun 9, 2026
Search Tags:#fine-tuning#LLM#LoRA#GRPO#DPO#axolotl
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill axolotl-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill axolotl-zhouboyu-xreal

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 YAML configurations, extensive model options, and support for LoRA, DPO, GRPO, and multimodal adjustments.

Core Features & Use Cases

  • Fine-Tuning Support: Offers comprehensive assistance with Axolotl's development, including YAML configurations, 100+ models, and various fine-tuning techniques.
  • Use Case: Fine-tune a large language model for a specific task, such as question answering or text generation, using Axolotl's robust configuration options and model support.

Quick Start

Use the axolotl skill to fine-tune a model with the specified parameters and configurations.

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 with LoRA and DPO using axolotl?▼

You can fine-tune an LLM using axolotl by configuring YAML files to apply techniques like LoRA and DPO. This Skill provides guidance on setting parameters and leveraging configurations to tailor models for specific tasks such as text generation.

What is GRPO and how does it work for large language model training?▼

GRPO is a fine-tuning technique supported by axolotl for adjusting large language models. It works alongside methods like DPO and LoRA within YAML configurations to refine model behavior and optimize training outcomes.

Can I use axolotl for multimodal training with transformers and deepspeed?▼

Yes, axolotl supports multimodal training and integrates with transformers, deepspeed, and accelerate. These dependencies enable distributed processing and efficient handling of complex training workflows across 100+ models.

Do I need peft and datasets to run axolotl fine-tuning procedures?▼

Yes, you need peft and datasets along with torch, transformers, accelerate, and deepspeed to run axolotl. These dependencies are required to execute the training procedures and manage the data inputs for fine-tuning.

What's the best way to configure YAML files for LLM fine-tuning?▼

The best way to configure YAML files for LLM fine-tuning is by using axolotl's configuration options to specify model parameters, training techniques like LoRA, and dataset mappings. This Skill offers guidance for setting up these robust configurations.

Why use axolotl over other tools for fine-tuning large language models?▼

Axolotl distinguishes itself by supporting 100+ models, multimodal adjustments, and advanced techniques like GRPO and DPO within a single YAML-driven workflow. It provides comprehensive configuration options for developers tailoring LLMs to specific applications.

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