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axolotl

Generate and manage YAML configurations for Axolotl fine-tuning of LLMs.

Updated Jun 17, 2026
Search Tags:#llm#machine learning#fine-tuning#training#lora#axolotl
One-click install
npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill axolotl-cxnaive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill axolotl-cxnaive

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the complex process of fine-tuning Large Language Models (LLMs) by providing a structured, configuration-driven interface for Axolotl, eliminating the need for manual script management.

Core Features & Use Cases

  • Configuration Management: Easily generate and manage YAML configurations for LoRA, QLoRA, DPO, and other fine-tuning methods.
  • Training Orchestration: Streamline the execution of training jobs across various hardware setups, from local GPUs to cloud clusters.
  • Use Case: If you need to fine-tune a Llama 3 model on a custom instruction dataset, use this Skill to validate your YAML config, set up the appropriate prompt strategy, and launch the training process with optimized parameters.

Quick Start

Use the axolotl skill to generate a training configuration for a Llama 3 model using LoRA and the Alpaca dataset format.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I generate a YAML configuration for fine-tuning a Llama 3 model with LoRA?▼

To generate a YAML configuration for Llama 3 fine-tuning, you define the base model, dataset, and LoRA parameters within a YAML file. This configuration-driven approach manages Axolotl training without manual scripts.

Can I use Axolotl for preference-based post-training like DPO and GRPO?▼

Yes, you can use Axolotl for preference-based post-training. It supports DPO and GRPO methodologies, providing specialized tokenization and prompt strategies for supervised fine-tuning and preference-based training.

What's the best way to fine-tune a Large Language Model across different hardware environments?▼

The best way to fine-tune Large Language Models across hardware environments is using YAML-based configuration management. This orchestrates training jobs from local GPUs to cloud clusters using DeepSpeed and accelerate.

Does Axolotl work with HuggingFace datasets for LLM training?▼

Yes, Axolotl works with HuggingFace datasets for LLM training. It integrates directly with the datasets library to provide data loading and specialized tokenization for supervised fine-tuning tasks.

Do I need torch and transformers to run Axolotl for QLoRA training?▼

Yes, you need torch and transformers to run Axolotl for QLoRA training. The framework depends on these libraries alongside peft and accelerate to execute parameter-efficient fine-tuning jobs.

Why use YAML-based configuration management for LLM fine-tuning instead of manual scripts?▼

You use YAML-based configuration management for LLM fine-tuning to eliminate manual script management. It provides a structured interface to validate parameters, set prompt strategies, and launch optimized training.

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