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axolotl

Fine-tune large language models using YAML-based training configurations.

1|Updated Jul 31, 2026
Search Tags:#llm#machine learning#fine-tuning#yaml#lora#axolotl
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill axolotl-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/icyzh/hermes-web --skill axolotl-icyzh

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 (LLMs) by providing a unified, configuration-driven interface for managing training parameters, datasets, and hardware optimizations.

Core Features & Use Cases

  • Unified Configuration: Manage LoRA, QLoRA, DPO, and GRPO training runs using simple YAML files.
  • Advanced Optimization: Access expert-level patterns for FSDP, DeepSpeed, and sequence parallelism to maximize GPU efficiency.
  • Use Case: Quickly configure a fine-tuning job for a Llama-3 model using QLoRA, ensuring optimal batch sizes and context parallelism without writing custom training loops.

Quick Start

Use the axolotl skill to generate a training configuration for a Llama-3 model using LoRA and the Alpaca dataset format.

Frequently Asked Questions about axolotl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure large language model fine-tuning without writing custom training loops?▼

Fine-tune large language models by defining training parameters, datasets, and hardware optimizations in YAML files. This configuration-driven approach manages SFT, DPO, and GRPO workflows without requiring custom training loops.

Can I use LoRA and QLoRA methodologies for training a Llama-3 model?▼

Yes, LoRA and QLoRA are supported for training models like Llama-3. The system provides specialized kernels for LoRA and QLoRA performance enhancements, enabling efficient configuration of batch sizes and context parallelism.

Does this approach support advanced GPU optimization frameworks like DeepSpeed and FSDP?▼

Yes, it supports advanced GPU optimization frameworks like DeepSpeed and FSDP. It provides expert-level patterns for FSDP, DeepSpeed, and sequence parallelism to maximize hardware efficiency across diverse architectures.

What is the best way to manage SFT, DPO, and GRPO training runs across different hardware?▼

Manage SFT, DPO, and GRPO training runs through a unified configuration interface. It abstracts complex training workflows across various hardware architectures, supporting diverse methodologies using simple YAML files.

Do I need to prepare datasets in a specific format before integrating them for LLM training?▼

You need to integrate datasets via Hugging Face datasets for LLM training. You specify dataset formats, such as the Alpaca format, directly within your YAML configuration to streamline the data loading pipeline.

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