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axolotl

Fine-tune large language models with Axolotl using YAML configurations.

Updated May 13, 2026
Search Tags:#fine-tuning#LLM#multimodal#YAML#HuggingFace#LoRA#DeepSpeed#GRPO#DPO#Axolotl
One-click install
npx skills add https://github.com/skkeoriw/hermes-brain --skill axolotl-skkeoriw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/skkeoriw/hermes-brain/tree/main/brain/hermes-home/skills/mlops/training/axolotl
Command: npx skills add https://github.com/skkeoriw/hermes-brain --skill axolotl-skkeoriw

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 large language models (LLMs) with Axolotl, enabling users to apply LoRA, DPO, GRPO, and support for YAML and HuggingFace formats.

Core Features & Use Cases

  • Fine-Tuning: Offers comprehensive assistance with axolotl development, including YAML configurations, model training, and debugging.
  • Features: Supports LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, and multimodal support.
  • Use Case: Ideal for researchers, developers, and data scientists working on LLM fine-tuning, requiring access to documentation and code examples.

Quick Start

Run the axolotl skill to fine-tune an LLM using the following command: axolotl train --config path/to/config.yaml.

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 a YAML configuration file?▼

You can fine-tune an LLM by defining your parameters in a YAML configuration file and executing the training command. This approach supports HuggingFace model formats and integrates with DeepSpeed for optimized performance.

What is the difference between LoRA, DPO, and GRPO for LLM fine-tuning?▼

LoRA is a parameter-efficient fine-tuning method, while DPO and GRPO are preference optimization techniques. This Skill provides guidance for configuring and applying all three methods within your YAML training setup.

Do I need DeepSpeed and accelerate to train large language models?▼

DeepSpeed and accelerate are required dependencies for distributed training and memory optimization when fine-tuning large language models. They enable efficient handling of substantial model sizes during the training process.

Can I use HuggingFace datasets for multimodal model training?▼

HuggingFace datasets are fully supported for multimodal model training. The setup allows you to load and process data directly from HuggingFace, integrating seamlessly with PyTorch and transformers for multimodal tasks.

How do I debug a failing axolotl training run?▼

Debugging a failing axolotl training run involves checking your YAML configurations, verifying dataset formats, and ensuring compatibility across torch, transformers, and peft libraries to resolve common training errors.

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