axolotl

Configure Axolotl YAML for LLM fine-tuning and evaluation workflows.

3|Updated Apr 4, 2026

Search Tags:#multimodal #distributed training #lora #qlora #axolotl #llm fine-tuning #yaml config

Ready to run

npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill axolotl-twjarviszyra-web

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/twjarviszyra-web/hermes-unbound/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/twjarviszyra-web/hermes-unbound --skill axolotl-twjarviszyra-web

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Axolotl helps you turn a model and dataset into a reliable training workflow without manually wiring together tokenization, masking, distributed training, or evaluation logic.

Core Features & Use Cases

Model Adaptation: Fine-tune language models with LoRA, QLoRA, FSDP, DeepSpeed, and other training strategies.
Dataset Handling: Work with pretraining, instruction, chat, preference, stepwise, and multimodal dataset formats.
Training Lifecycle: Cover preprocessing, training, inference, merging, and post-training quantization from one config-driven workflow.
Use Case: A research team can use this Skill to prepare a custom dataset, launch a multi-GPU fine-tune, validate outputs, and export a merged model for deployment.

Quick Start

Ask for an Axolotl YAML configuration tailored to your model, dataset format, and training goal.

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I configure LLM fine-tuning without manually wiring tokenization and distributed training logic?▼

You can use a YAML configuration file to drive LLM fine-tuning, which abstracts away manual tokenization, masking, and distributed training setup. This approach allows you to reliably define training workflows by specifying your model, dataset format, and tuning parameters in one place.

Can I use LoRA and QLoRA for language model adaptation across multiple GPUs?▼

Yes, configuration-driven fine-tuning supports LoRA, QLoRA, FSDP, and DeepSpeed strategies for model adaptation. It enables you to launch multi-GPU distributed training workflows seamlessly by defining the desired training backend and strategy within your YAML configuration.

What dataset formats are supported for LLM supervised fine-tuning and preference optimization?▼

LLM fine-tuning supports pretraining, instruction, chat, preference, stepwise, and multimodal dataset formats. You can supply Hugging Face-style datasets directly into your configuration to handle preprocessing and masking for supervised fine-tuning or preference optimization workflows.

Does Axolotl support multimodal model training and inference?▼

Yes, Axolotl supports multimodal adaptation and inference alongside standard language model workflows. You can configure multimodal dataset formats and training parameters in your YAML file to fine-tune vision-language models using the same configuration-driven lifecycle.

What is the best way to merge and quantize a fine-tuned LLM for deployment?▼

The best way to merge and quantize a fine-tuned LLM is through a unified training lifecycle that handles post-training export natively. After running your fine-tuning workflow, you can use the same configuration tooling to merge adapters and apply quantization before deployment.

Do I need DeepSpeed or FSDP for multi-GPU LLM fine-tuning?▼

You do not strictly need DeepSpeed or FSDP, but they are supported as PyTorch training backends for distributed GPU setups. You can select your preferred distributed training strategy within the YAML configuration to match your hardware scale and fine-tuning requirements.