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axolotl

Design and execute LLM fine-tuning experiments with Axolotl YAML configs.

1|Updated Mar 22, 2026
Search Tags:#mlops#llm#fine-tuning#lora#qlora#rlhf#axolotl
One-click install
npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill axolotl-nelohenriq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/nelohenriq/hermes-agent-plus/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/nelohenriq/hermes-agent-plus --skill axolotl-nelohenriq

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Enables researchers and engineers to design, configure, and execute large language model fine-tuning experiments with Axolotl, covering YAML configs, adapters, and RLHF workflows.

Core Features & Use Cases

  • YAML-driven training configurations for scalable LLM fine-tuning.
  • Support for LoRA/QLoRA adapters, DPO/KTO/ORPO/GRPO RLHF methods, and multimodal setups.
  • Use Case: rapidly prototype and compare fine-tuning strategies across multiple models and datasets.

Quick Start

Load an Axolotl config and run the trainer to begin fine-tuning an LLM with LoRA 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 with LoRA and QLoRA adapters?▼

LLM fine-tuning with LoRA and QLoRA adapters is driven by YAML configurations that define training parameters, dataset paths, and adapter settings. You load an Axolotl config file and execute the trainer to begin scalable model adaptation.

What is the best way to run DPO, ORPO, or GRPO preference alignment workflows?▼

DPO, ORPO, and GRPO are RLHF methods supported for preference alignment within Axolotl workflows. You configure these alignment strategies through YAML-driven settings to rapidly prototype and compare fine-tuning approaches across datasets.

Can I use Axolotl for multimodal RLHF setups with DeepSpeed and Accelerate?▼

Multimodal RLHF setups are supported in Axolotl, leveraging dependencies like DeepSpeed and Accelerate to scale training. This enables researchers to design and execute complex fine-tuning experiments across text and other modalities.

How does PyTorch and PEFT integrate with Axolotl for large language model training?▼

PyTorch and PEFT integrate as core dependencies to enable parameter-efficient fine-tuning of large language models. Axolotl orchestrates these libraries to manage LoRA and QLoRA adapter training through scalable YAML configurations.

Do I need specific datasets and transformers libraries to start fine-tuning with Axolotl?▼

You need the transformers and datasets libraries to format and load training data for Axolotl fine-tuning. These dependencies are required to process datasets and manage model architectures within the YAML-configured MLOps pipeline.

What are the limitations when comparing Axolotl to other LLM fine-tuning tools?▼

Axolotl focuses specifically on YAML-configured training, adapter support, and RLHF workflows for large language models. Users requiring framework-agnostic customization or non-PyTorch backend support may need alternative MLOps solutions.

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