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axolotl

Generate Axolotl documentation for training, RLHF, and fine-tuning workflows.

Updated Mar 26, 2026
Search Tags:#llm#fine-tuning#lora#dpo#rlhf#axolotl
One-click install
npx skills add https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11 --skill axolotl-cloudliness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/cloudliness/Hermes-Autonomous-AI-Agent-Dialed-In-For-Windows-11 --skill axolotl-cloudliness

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Axolotl provides structured guidance and reference material to help researchers and engineers adopt, configure, and operate Axolotl for training, RLHF, and model customization, reducing the time to get from theory to working pipelines.

Core Features & Use Cases

  • Comprehensive API and dataset-reference coverage, including prompts, trainers, and utilities.
  • Ready-made templates for common Axolotl workflows (pretraining, supervised fine-tuning, and RLHF) with example code snippets and configuration guidance.
  • On-demand access to detailed reference materials for API surfaces and dataset formats to accelerate integration and experimentation.

Quick Start

Run the Axolotl training workflow by providing a YAML config to the training CLI to begin processing and training tasks.

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 a YAML config with axolotl?▼

To fine-tune an LLM using axolotl, you provide a YAML configuration file to the training CLI to initiate the supervised fine-tuning or pretraining processing tasks.

Does axolotl support RLHF and DPO training workflows?▼

Yes, axolotl supports RLHF and DPO training workflows by providing ready-made templates and reference materials for reward-modeling and alignment pipelines.

Can I use axolotl with DeepSpeed and PEFT for efficient training?▼

Yes, you can use axolotl with DeepSpeed and PEFT dependencies to accelerate training and apply parameter-efficient fine-tuning methods.

What dataset formats are supported for LLM fine-tuning in axolotl?▼

Axolotl provides detailed reference materials for various dataset formats and API surfaces to help structure datasets correctly for supervised fine-tuning and pretraining.

What is the best way to configure pretraining and SFT pipelines in axolotl?▼

The best way to configure pretraining and SFT pipelines is by using axolotl's ready-made templates and configuration guidance to quickly build working training pipelines.

Why do I need torch and transformers to run axolotl workflows?▼

You need torch and transformers because axolotl depends on these frameworks to execute its underlying machine learning training workflows and model processing tasks.

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