axolotl

Name: axolotl
Availability: InStock
Author: Quill-Agent

Fine-tune large language models with LoRA, DPO, and GRPO via YAML configuration.

3|1|Updated May 19, 2026

Search Tags:#llm #fine-tuning #yaml #axolotl #large language model

Ready to run

npx skills add https://github.com/Quill-Agent/Quill-Agent --skill axolotl-quill-agent

💬 Prompt for ChatGPT / Claude Chat▼

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

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?

Axolotl addresses the challenge of fine-tuning LLMs (Large Language Models) with a comprehensive suite of tools and documentation, reducing the complexity and time required for this process.

Core Features & Use Cases

YAML Fine-Tuning: Offers YAML configuration for LoRA, DPO, GRPO, and other techniques.
Comprehensive Documentation: Provides detailed guides, code examples, and best practices.
Multi-Platform Support: Compatible with Linux, macOS, and other platforms.
Use Case: Fine-tune a LLM model for a specific application like code generation, text summarization, or chatbot interactions.

Quick Start

Run the axolotl skill with the following command: axolotl fine-tune --model llama --config config.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 with YAML configurations?▼

You can fine-tune a large language model using YAML configurations by defining your training parameters in a config file and executing the command line tool. This approach simplifies setting up LoRA, DPO, and GRPO training workflows.

Does LLM fine-tuning with axolotl work on macOS and Linux?▼

LLM fine-tuning with axolotl works across Linux, macOS, and other compatible platforms. This multi-platform support allows you to train models like Llama in diverse development environments without changing configurations.

Can I use DeepSpeed and PEFT for large language model fine-tuning?▼

Yes, you can use DeepSpeed and PEFT for large language model fine-tuning. The environment requires these dependencies alongside torch, transformers, datasets, and accelerate to efficiently execute training scripts and manage memory.

What's the best way to configure LoRA and DPO for LLM training?▼

The best way to configure LoRA and DPO for LLM training is through a centralized YAML file. This method provides comprehensive documentation and code examples to establish optimal parameters for specific applications.

What dependencies do I need to install before fine-tuning a LLM?▼

Before fine-tuning a LLM, you need to install axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed. These dependencies provide the foundational framework required to process datasets and execute the training routines.