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axolotl

Fine-tune large language models using YAML configuration files.

Updated Jul 13, 2026
Search Tags:#llm#machine learning#fine-tuning#training#lora#axolotl
One-click install
npx skills add https://github.com/zangjeicy/Hermes --skill axolotl-zangjeicy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/zangjeicy/Hermes --skill axolotl-zangjeicy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires axolotl, torch, transformers, datasets, peft, accelerate, deepspeed, and includes references (resource) components.

What problem does it solve?

This Skill simplifies the complex process of fine-tuning Large Language Models by providing a unified, configuration-driven interface that handles everything from data preparation to training execution.

Core Features & Use Cases

  • Unified Configuration: Manage LoRA, QLoRA, DPO, and GRPO training runs using simple YAML files.
  • Multimodal Support: Easily integrate vision and language models for advanced training tasks.
  • Use Case: A researcher needs to fine-tune a Llama 3 model on a custom instruction dataset. Instead of writing custom training loops, they use this Skill to define the dataset path, model parameters, and LoRA settings in a single YAML file to launch the training process.

Quick Start

Use the axolotl skill to initiate a fine-tuning run using the configuration file located at configs/my-model-train.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 Llama 3 model using a YAML configuration?▼

You can fine-tune Llama 3 by defining dataset paths, model parameters, and training settings in a single YAML file, allowing a standardized interface to handle the execution without writing custom training loops.

What is the best way to run LoRA and QLoRA training without writing custom training loops?▼

Using a unified YAML-based configuration interface lets you manage LoRA, QLoRA, DPO, and GRPO training runs directly, streamlining the fine-tuning process across various hardware environments.

Does axolotl support multimodal vision and language models?▼

Yes, multimodal support is integrated, allowing you to easily combine vision and language models for advanced training tasks through the same configuration-driven workflow.

Can I use DeepSpeed and accelerate for scalable LLM training?▼

Yes, the workflow integrates DeepSpeed and accelerate as essential dependencies to ensure robust and scalable model training across diverse hardware environments.

What are the prerequisites to start fine-tuning large language models with this approach?▼

You need PyTorch, transformers, datasets, and PEFT installed, as these are the core dependencies integrated to facilitate robust model training and dataset preparation.

How do I launch a fine-tuning run using a specific configuration file?▼

You can initiate a fine-tuning run by specifying your YAML configuration file path, such as configs/my-model-train.yaml, to automatically process dataset preparation and training execution.

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