axolotl

Name: axolotl
Availability: InStock
Author: zeronx

Fine-tune Large Language Models using YAML configurations and automated training workflows.

Updated Jul 13, 2026

Search Tags:#llm #machine learning #fine-tuning #training #lora #axolotl

Ready to run

npx skills add https://github.com/zeronx798/demo-hermes-agent --skill axolotl-zeronx798

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/zeronx798/demo-hermes-agent/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/zeronx798/demo-hermes-agent --skill axolotl-zeronx798

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires axolotl, torch, transformers, datasets, peft, accelerate, deepspeed, and includes references (resource) components.

What problem does it solve?

This skill simplifies the complex process of fine-tuning Large Language Models (LLMs) by providing a unified interface for managing YAML configurations, model training, and advanced optimization techniques.

Core Features & Use Cases

Comprehensive Fine-Tuning: Supports LoRA, QLoRA, DPO, KTO, ORPO, and GRPO training methods.
Model Optimization: Provides tools for quantization, FSDP configuration, and performance testing.
Use Case: When you need to fine-tune a Llama 3 model on a custom dataset, use this skill to generate the correct YAML configuration, validate your training parameters, and execute the training process efficiently.

Quick Start

Use the axolotl skill to generate a training configuration for a Llama 3 model using LoRA and start the fine-tuning process.

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 Llama 3 model using LoRA on a custom dataset?▼

To fine-tune a Llama 3 model with LoRA, you generate a standardized YAML configuration, validate your training parameters, and execute the automated training workflow. This skill simplifies the process by integrating transformers and peft libraries to manage the custom dataset training.

What training paradigms are supported for large language model optimization?▼

Supported large language model optimization paradigms include supervised fine-tuning, LoRA, QLoRA, DPO, KTO, ORPO, and GRPO. These methods enable scalable and reproducible model development across various hardware environments using deepspeed and accelerate.

Can I use deepspeed and accelerate for scalable LLM training workflows?▼

Yes, you can use deepspeed and accelerate for scalable LLM training workflows. This skill integrates these essential libraries to ensure scalable and reproducible model development across diverse hardware environments during fine-tuning.

What is the best way to manage YAML configurations for LLM training?▼

The best way to manage YAML configurations for LLM training is using a unified interface that standardizes parameter validation and automated workflows. This approach simplifies complex fine-tuning processes by integrating libraries like transformers and peft.

How do I quantize a large language model and configure FSDP?▼

You can quantize a large language model and configure FSDP using the built-in model optimization tools. This skill provides tools for quantization and FSDP configuration to ensure efficient performance testing and scalable training.