axolotl

Name: axolotl
Availability: InStock
Author: Khyro

Configure and validate Axolotl YAML files for model fine-tuning workflows.

Updated Apr 2, 2026

Search Tags:#llm #yaml #lora #finetuning #qlora #dpo #axolotl

Ready to run

npx skills add https://github.com/JKhyro/HERMES-AGENT --skill axolotl-jkhyro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/JKhyro/HERMES-AGENT/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/JKhyro/HERMES-AGENT --skill axolotl-jkhyro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you configure, troubleshoot, and operate Axolotl for model training workflows without having to piece together the documentation yourself.

Core Features & Use Cases

Training Setup: Build and validate YAML configs for supervised fine-tuning, pretraining, and preference optimization.
Advanced Training Workflows: Work with LoRA, QLoRA, FSDP, multimodal models, quantization, and RLHF-style training.
Inference and Maintenance: Prepare models for inference, merging, preprocessing, and dataset-format selection.
Use Case: A machine learning engineer can use this Skill to turn a dataset and target model into a working fine-tuning plan, then resolve common configuration and runtime issues.

Quick Start

Ask for help creating or debugging an Axolotl config for your model, dataset, and training goal.

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I configure a YAML file for fine-tuning large language models with LoRA or QLoRA?▼

Fine-tuning large language models with LoRA or QLoRA requires a YAML-based configuration to define training parameters, model setup, and dataset paths. This workflow supports supervised fine-tuning, quantization, and chat-template-aware datasets.

Can I use Axolotl for preference optimization methods like DPO, KTO, ORPO, and GRPO?▼

Yes, Axolotl supports preference optimization training including DPO, KTO, ORPO, and GRPO. You can configure these RLHF-style training workflows through YAML configuration files tailored to your specific model and dataset requirements.

What's the best way to set up multimodal training and FSDP in an Axolotl config?▼

Setting up multimodal training and FSDP involves specifying the appropriate parameters within your Axolotl YAML configuration. This enables distributed training across multiple GPUs and supports multimodal model architectures for complex training scenarios.

Does Axolotl support dataset preprocessing and chat-template-aware datasets for supervised fine-tuning?▼

Yes, Axolotl supports dataset preprocessing and chat-template-aware datasets for supervised fine-tuning. You can select dataset formats and preprocess data directly within your YAML configuration to ensure proper tokenizer setup.

How do I prepare a fine-tuned model for inference and merging after training completes?▼

Preparing a fine-tuned model for inference and merging involves post-training steps configured through Axolotl. You can merge LoRA adapters with the base model and set up inference endpoints using the same YAML-based workflow.

Why is my Axolotl training config not working during model and tokenizer setup?▼

Axolotl training config issues during model and tokenizer setup often stem from incorrect YAML parameters, mismatched chat templates, or dataset format errors. Troubleshooting involves validating the configuration against your specific training goal.