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axolotl

Configure and execute LLM fine-tuning workflows with the Axolotl framework.

1|Updated Apr 24, 2026
Search Tags:#multimodal#lora#huggingface#dpo#grpo#axolotl#llm fine-tuning
One-click install
npx skills add https://github.com/automatedigital/spark --skill axolotl-automatedigital
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/automatedigital/spark --skill axolotl-automatedigital

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tuning large language models requires navigating complex configuration options, distributed training setups, and specialized fine-tuning methods, creating a steep learning curve for developers and ML engineers. This Skill eliminates that friction by providing expert, documentation-backed guidance for using Axolotl, the popular open-source LLM fine-tuning framework.

Core Features & Use Cases

  • Comprehensive Axolotl Guidance: Covers YAML config generation, support for 100+ HuggingFace models, and all major fine-tuning methods including LoRA, QLoRA, DPO, KTO, ORPO, and GRPO.
  • Reference Documentation: Includes full API references, dataset format guides for SFT, pre-training, and RLHF workflows, and troubleshooting resources for common training issues.
  • Use Case: A developer fine-tuning a Llama 3 model with LoRA for a customer support chatbot can use this Skill to quickly configure training parameters, format their custom instruction dataset, and resolve NCCL communication errors during multi-GPU training.

Quick Start

Use the axolotl skill to create a valid YAML configuration for fine-tuning your Mistral model with DPO on a custom preference dataset.

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 for a Llama 3 model with LoRA?▼

LLM fine-tuning with LoRA requires generating a YAML configuration file to define training parameters and format your custom instruction dataset. Axolotl supports this workflow, enabling adapter configuration for models like Llama 3.

Does Axolotl support multimodal fine-tuning and HuggingFace models?▼

Axolotl supports multimodal fine-tuning and integrates directly with over 100 HuggingFace models. This allows you to apply various fine-tuning methods to diverse model architectures without leaving the framework.

How do I format datasets for DPO and RLHF workflows?▼

Dataset formatting for DPO and RLHF workflows requires structuring custom preference data according to specific schema guidelines. Axolotl provides reference documentation and dataset format guides for pre-training, SFT, and preference optimization.

Can I set up distributed training with FSDP and DeepSpeed for LLM fine-tuning?▼

Distributed training for LLM fine-tuning can be set up using FSDP and DeepSpeed configurations. Axolotl enables this environment setup to scale training across multiple GPUs while providing troubleshooting resources for communication errors.

What is the best way to resolve NCCL communication errors during multi-GPU training?▼

Resolving NCCL communication errors during multi-GPU training involves checking your distributed training setup and network configurations. Axolotl includes troubleshooting resources to address common training issues like these.

Can I integrate vLLM and MLflow into my LLM fine-tuning workflow?▼

You can integrate vLLM and MLflow into your LLM fine-tuning workflow to handle inference and experiment tracking. Axolotl supports these integrations alongside GRPO, KTO, and ORPO optimization methods.

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