axolotl

Name: axolotl
Availability: InStock
Author: Aayushsoam

Fine-tune Large Language Models with Axolotl using YAML configurations.

1|1|Updated May 25, 2026

Search Tags:#machine learning #large language models #axolotl #llm fine-tuning #yaml configuration

Ready to run

npx skills add https://github.com/aayushsoam/clawbot-agent --skill axolotl-aayushsoam

💬 Prompt for ChatGPT / Claude Chat▼

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

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 with Axolotl, making complex tasks like LoRA, DPO, and GRPO accessible with YAML configurations.

Core Features & Use Cases

LLM Fine-Tuning: Offers comprehensive support for fine-tuning with Axolotl, including LoRA, DPO, and GRPO techniques.
YAML Configuration: Utilizes YAML for simple and readable configuration files, making it easy to manage and adjust model parameters.
Use Case: Ideal for researchers and developers who want to fine-tune models with advanced techniques like LoRA or DPO without delving into complex code.

Quick Start

To get started, run the axolotl skill with your desired model and configuration. For example: axolotl --model my_model.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 a large language model using YAML configuration?▼

You can fine-tune large language models using YAML configuration by defining your model parameters and training preferences in a readable YAML file. This approach allows you to manage and adjust model optimization settings without writing complex training code.

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

LoRA, DPO, and GRPO are distinct fine-tuning techniques for large language models. LoRA focuses on low-rank adaptation for efficient parameter tuning, while DPO and GRPO offer different alignment and optimization strategies, all configurable via YAML to suit specific research and development needs.

Do I need PyTorch and Hugging Face libraries to run LLM fine-tuning with LoRA?▼

Yes, you need PyTorch and Hugging Face libraries to run LLM fine-tuning with LoRA. The process requires dependencies including torch, transformers, datasets, peft, accelerate, and deepspeed to properly manage model optimization and configuration.

Can I use DeepSpeed and Accelerate for LLM fine-tuning optimization?▼

Yes, you can use DeepSpeed and Accelerate for LLM fine-tuning optimization. These libraries are included as core dependencies to help manage and scale the training process effectively when working with large language models and complex YAML configurations.

What's the best way to configure model parameters for DPO without writing complex code?▼

The best way to configure model parameters for DPO without complex code is using YAML configuration files. This method provides a simple and readable format to manage and adjust model parameters, making advanced fine-tuning techniques accessible to data scientists.

Why use YAML configuration for large language model optimization?▼

YAML configuration is used for large language model optimization because it provides a simple and readable format for managing parameters. It allows data scientists and machine learning engineers to easily adjust settings for techniques like LoRA and DPO without delving into complex code.