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axolotl

Fine-tune large language models using Axolotl YAML configurations and training scripts.

539|39|Updated May 1, 2026
Search Tags:#fine-tuning#LLM#LoRA#DeepSpeed#QLoRA#Axolotl
One-click install
npx skills add https://github.com/Tommy-yw/RunbookHermes --skill axolotl-tommy-yw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/Tommy-yw/RunbookHermes/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/Tommy-yw/RunbookHermes --skill axolotl-tommy-yw

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, enabling efficient configuration management, model selection, and advanced fine-tuning techniques like LoRA and QLoRA.

Core Features & Use Cases

  • YAML Configurations: Access pre-defined Axolotl configurations for different LLM models.
  • 100+ Models Supported: Utilize a vast selection of LLMs to fine-tune according to specific requirements.
  • LoRA/QLoRA: Implement LoRA and QLoRA for parameter-efficient fine-tuning.
  • Multimodal Support: Leverage multimodal capabilities to fine-tune LLMs with additional data types like images or audio.
  • Use Case: Fine-tune a large language model to perform a specific task, such as text classification or language generation, by using the appropriate model configuration and LoRA parameters.

Quick Start

Run the following command to fine-tune a model using Axolotl:

axolotl train --config path/to/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 an LLM using Axolotl configurations?▼

Axolotl supports parameter-efficient fine-tuning through LoRA and QLoRA techniques. These methods allow you to adapt large language models by training a small number of parameters, reducing computational overhead while maintaining model performance for specific tasks.

Can I use DeepSpeed and PyTorch for LLM training with Axolotl?▼

Yes, Axolotl provides multimodal support for fine-tuning large language models. You can leverage this capability to train models using additional data types like images or audio alongside text, expanding the scope of applicable NLP tasks.

What's the best way to configure LoRA parameters for text classification models?▼

Axolotl requires several Python libraries to function properly. You need to install axolotl, torch, transformers, datasets, peft, accelerate, and deepspeed to ensure the environment supports model configuration and training scripts.

Does Axolotl support multimodal fine-tuning for models with image data?▼

Yes, Axolotl supports multimodal fine-tuning. You can leverage its multimodal capabilities to fine-tune large language models with additional data types like images or audio, enabling broader NLP task coverage beyond standard text processing.

Do I need to install transformers and peft to run axolotl train scripts?▼

Axolotl supports over 100 large language models for fine-tuning. You can select from this vast selection of LLMs and utilize pre-defined YAML configurations to adapt the chosen model according to your specific task requirements.

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