axolotl

Name: axolotl
Availability: InStock
Author: ldzhhxx

Guide fine-tuning of LLMs with Axolotl using LoRA, DPO, and GRPO.

1|1|Updated May 9, 2026

Search Tags:#fine-tuning #LLM #YAML #HuggingFace #LoRA #DeepSpeed #GRPO #DPO #Axolotl #Multimodal

Ready to run

npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill axolotl-ldzhhxx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/skills/mlops/training/axolotl
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill axolotl-ldzhhxx

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) using Axolotl, supporting various techniques like LoRA, DPO, and GRPO, and offering comprehensive documentation and examples.

Core Features & Use Cases

Fine-Tuning Support: Offers guidance on fine-tuning LLMs with Axolotl, including LoRA, DPO, and GRPO.
Documentation: Includes detailed documentation on Axolotl's APIs, dataset formats, and best practices.
Examples: Provides code examples for common use cases, such as setting up configurations and handling data.
Use Case: Ideal for developers and researchers looking to improve the performance of their LLMs through advanced fine-tuning techniques.

Quick Start

Run the skill with the command: axolotl fine-tune --model <path_to_model> --dataset <path_to_dataset> --method <fine_tuning_method>

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 LoRA, DPO, and GRPO?▼

You can fine-tune Large Language Models using LoRA, DPO, and GRPO by configuring YAML files and running the axolotl CLI with your specified model and dataset paths. This Skill provides expert guidance, comprehensive documentation, and code examples for these advanced techniques.

What is the best way to configure YAML files for LLM fine-tuning?▼

Configuring YAML files for LLM fine-tuning involves defining model paths, dataset formats, and training methods like LoRA or DPO. This Skill provides detailed documentation on Axolotl's APIs and code examples to help you set up configurations and handle data correctly.

Do I need DeepSpeed and HuggingFace transformers to run axolotl?▼

Yes, you need DeepSpeed and HuggingFace transformers along with torch, datasets, peft, and accelerate as dependencies. These frameworks provide the necessary environment for executing advanced fine-tuning processes and managing model performance.

Can I use axolotl for multimodal LLM fine-tuning?▼

Yes, axolotl supports multimodal LLM fine-tuning alongside text-based models. It provides the necessary tools and configurations to handle diverse data formats and training requirements for multimodal applications.

How do I start fine-tuning an LLM with axolotl from the command line?▼

You start fine-tuning an LLM by running the command: axolotl fine-tune --model <path_to_model> --dataset <path_to_dataset> --method <fine_tuning_method>. This initiates the training process using your specified configurations.