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axolotl

Fine-tune LLMs with Axolotl using YAML configurations and LoRA/QLoRA workflows.

Updated Mar 22, 2026
Search Tags:#llm#fine-tuning#multimodal#lora#qlora#rlhf#axolotl
One-click install
npx skills add https://github.com/ChimeraFoundationa/Agentx --skill axolotl-chimerafoundationa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/ChimeraFoundationa/Agentx/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/ChimeraFoundationa/Agentx --skill axolotl-chimerafoundationa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl helps teams effectively fine-tune large language models using YAML-based configurations, enabling scalable experiments with LoRA/QLoRA, DPO, KTO, ORPO, and GRPO in multimodal contexts.

Core Features & Use Cases

  • Guided, end-to-end guidance for Axolotl fine-tuning workflows on various model sizes and modalities.
  • Reference-driven learning with official Axolotl docs and a catalog of training strategies, prompts, and data formats.
  • Use cases include setting up LoRA-based adapters, configuring post-training reward models, and integrating with multi-provider datasets.

Quick Start

Provide a minimal YAML config to start fine-tuning an LLM with Axolotl and run the training pipeline.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I configure LoRA or QLoRA fine-tuning for a large language model?▼

Axolotl fine-tuning uses YAML configurations to guide LoRA and QLoRA workflows, enabling scalable experiments across various model sizes and multimodal contexts.

Can I apply DPO, ORPO, or GRPO post-training techniques to my LLM?▼

Yes, guided fine-tuning supports RLHF-based post-training techniques including DPO, ORPO, and GRPO, allowing you to apply preference optimization directly to your models.

What is the best way to start fine-tuning an LLM with a minimal setup?▼

The best way to start fine-tuning is by providing a minimal YAML configuration to define training parameters and running the training pipeline for your large language model.

Does this fine-tuning workflow support multimodal model contexts?▼

Yes, the fine-tuning workflow explicitly supports multimodal setups, enabling you to train and adapt models that process multiple data types simultaneously.

Do I need deepspeed and accelerate installed for LLM fine-tuning?▼

Yes, executing the fine-tuning pipeline requires dependencies including deepspeed, accelerate, torch, transformers, datasets, and peft to manage distributed training workflows.

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