Axolotl

Configure and execute LLM fine-tuning pipelines with YAML-based configurations.

577|62|Updated May 15, 2026

Search Tags:#mlops #multimodal #qlora #rlhf #loRA #llm-fine-tuning #axolotl

Ready to run

npx skills add https://github.com/agentic-in/elephant-agent --skill axolotl-agentic-in

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Axolotl
Source: https://github.com/agentic-in/elephant-agent/tree/main/packages/skills/builtin_packages/mlops/training/axolotl
Command: npx skills add https://github.com/agentic-in/elephant-agent --skill axolotl-agentic-in

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl provides expert guidance to configure and execute LLM fine-tuning pipelines with YAML-based configurations, supporting LoRA/QLoRA, DPO/KTO/ORPO/GRPO, and multimodal pipelines across 100+ models.

Core Features & Use Cases

Guided fine-tuning workflows across many models with adapter-based methods and RLHF variants.
Flexible pipeline design for multi-modal data and complex RLHF setups in ML Ops.
Real-world use case: adapting a base model to a company-specific style using adapters and reward modeling.

Quick Start

Install Axolotl and start a fine-tuning workflow using your chosen model and adapters.

Frequently Asked Questions about Axolotl

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

FAQPage Schema

How do I configure YAML files for LLM fine-tuning?▼

YAML configurations for LLM fine-tuning define model parameters, adapter methods, and dataset processing rules. Axolotl uses these YAML files to establish end-to-end training pipelines, supporting adapter patches and multimodal setups across 100+ models.

What is the difference between LoRA and QLoRA for model adaptation?▼

LoRA and QLoRA are adapter-based methods for parameter-efficient fine-tuning. This Skill configures both approaches within Axolotl workflows, allowing you to adapt base models to specific styles while managing memory constraints through quantization techniques.

Can I use RLHF variants like DPO and ORPO for reward modeling?▼

Yes, RLHF variants including DPO, KTO, ORPO, and GRPO are supported. The Skill provides guidance for configuring these reinforcement learning pipelines, enabling complex reward modeling setups within your ML Ops workflow for end-to-end training.

How do I set up multimodal fine-tuning pipelines?▼

Multimodal pipelines are configured through YAML-based setups that process multi-modal data inputs. The Skill guides flexible pipeline design for multimodal training across supported models, handling data processing and adapter patches required for these complex workflows.

Does Axolotl support custom adapter patches during training?▼

Yes, adapter patches are supported during LLM fine-tuning. The Skill encompasses adapter-based methods like LoRA and QLoRA, allowing you to apply custom patches within YAML configurations to adapt base models to company-specific styles or requirements.