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axolotl

Fine-tune LLMs with Axolotl using YAML configs and LoRA/QLoRA adapters.

Updated Apr 23, 2026
Search Tags:#llm#fine-tuning#multimodal#lora#qlora#dpo#axolotl
One-click install
npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill axolotl-chris-chai-minjae
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill axolotl-chris-chai-minjae

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 a structured framework to fine-tune large language models using YAML-based configurations, enabling efficient experimentation with LoRA/QLoRA, DPO, ORPO, GRPO, and multimodal setups.

Core Features & Use Cases

  • YAML-driven fine-tuning workflows that simplify model customization and replication.
  • Support for LoRA/QLoRA adapters, DPO/ORPO/GRPO RLHF methods, and multimodal training pipelines.
  • Real-world use: teams can rapidly prototype fine-tuning strategies on instruction-following models and compare configurations with built-in memory and reference materials.

Quick Start

Install Axolotl and run a basic training config to begin fine-tuning an LLM.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I fine-tune a large language model using YAML configurations?▼

Fine-tune a large language model using YAML configurations by defining training parameters, dataset paths, and adapter settings within a YAML file. This structured approach simplifies model customization and enables rapid replication of training experiments.

What is the best way to apply LoRA and QLoRA adapters for LLM training?▼

Apply LoRA and QLoRA adapters for LLM training by leveraging a framework that supports parameter-efficient fine-tuning. This enables rapid prototyping on instruction-following models while optimizing memory usage during experimentation.

Can I use DPO, ORPO, and GRPO for RLHF fine-tuning?▼

You can use DPO, ORPO, and GRPO for RLHF fine-tuning to align model outputs with human preferences. These methods are supported within the training pipeline to refine instruction-following capabilities and model behaviors.

Does the axolotl framework support multimodal training pipelines?▼

The framework supports multimodal training pipelines, allowing developers to fine-tune models that process multiple data types. This integration is driven by YAML-based configurations for structured experimentation and model customization.

Do I need deepspeed and accelerate to run axolotl fine-tuning?▼

You need deepspeed and accelerate to run fine-tuning efficiently. These dependencies, alongside torch and transformers, provide the distributed computing and memory optimization required for handling large language models.

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