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axolotl

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

Updated May 3, 2026
Search Tags:#YAML#LoRA#DeepSpeed#LLM fine-tuning#GRPO#DPO#axolotl
One-click install
npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill axolotl-eliottbusiness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/eliottbusiness/DeptFlow-Agent/tree/main/profile/skills/mlops/training/axolotl
Command: npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill axolotl-eliottbusiness

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 solves the complexity and technical hurdles associated with fine-tuning large language models using Axolotl, providing a straightforward approach to applying LoRA, DPO, GRPO, and other techniques for LLM fine-tuning.

Core Features & Use Cases

  • YAML LLM Fine-Tuning: Offers expert guidance for fine-tuning LLMs with Axolotl, including LoRA, DPO, GRPO, and multimodal support.
  • Development Assistance: Provides comprehensive assistance with axolotl development, generated from official documentation.
  • Use Case: Ideal for developers looking to implement advanced fine-tuning methods on LLMs with minimal overhead.

Quick Start

Use the axolotl skill to fine-tune an LLM model using YAML configurations and LoRA techniques.

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 configurations?▼

YAML LLM fine-tuning with Axolotl allows you to define training parameters declaratively, executing advanced methods like LoRA and DPO without writing extensive boilerplate code. It supports multi-modal data and integrates directly with HuggingFace and DeepSpeed.

Can I use DeepSpeed and PEFT for LLM fine-tuning with YAML?▼

Yes, YAML-based LLM fine-tuning supports DeepSpeed and PEFT natively. You can configure distributed training and parameter-efficient fine-tuning techniques directly within your YAML file, utilizing these dependencies to optimize memory usage and accelerate training speed.

What's the best way to apply DPO and GRPO techniques for LLM training?▼

Applying DPO and GRPO techniques is simplified through Axolotl's YAML configuration interface, which abstracts the complex underlying execution logic for preference alignment and reinforcement learning. This enables developers to implement advanced fine-tuning methods with minimal overhead.

Do I need PyTorch and Transformers installed to run YAML-based LLM fine-tuning?▼

Yes, YAML-based LLM fine-tuning requires PyTorch, Transformers, Datasets, PEFT, Accelerate, and DeepSpeed as core dependencies. These foundational libraries provide the necessary tensor operations, model architectures, and distributed training capabilities required to execute the fine-tuning pipelines.

Does YAML-based fine-tuning support multi-modal data training?▼

Yes, YAML LLM fine-tuning supports multi-modal data training configurations. You can specify multi-modal datasets within your YAML file to train models on combined text and image inputs, extending standard language model capabilities through Axolotl's native support.

Why use YAML configurations instead of writing custom PyTorch training scripts for LLM fine-tuning?▼

YAML configurations solve the technical hurdles and complexity of fine-tuning by providing a straightforward, declarative approach compared to writing custom PyTorch scripts. This allows developers to implement advanced methods like LoRA and GRPO with minimal overhead and reduced error risk.

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