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axolotl

Fine-tune large language models with YAML configurations for LoRA, DPO, and GRPO.

78|16|Updated Apr 23, 2026
Search Tags:#llm#fine-tuning#yaml#lora#dpo#grpo
One-click install
npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill axolotl-sheawinkler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/sheawinkler/hermes-agent-ultra/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill axolotl-sheawinkler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tune large language models using YAML-driven configurations for LoRA/QLoRA, DPO, and GRPO.

Core Features & Use Cases

  • YAML-based configuration for end-to-end fine-tuning across models and datasets.
  • Supports LoRA/QLoRA, DPO, ORPO, and GRPO; policy enforcement and memory backends.
  • Use cases include research pipelines, production-grade RLHF setups, and rapid prototyping.

Quick Start

Create a YAML config describing your fine-tuning plan and run the trainer to begin YAML-driven LLM fine-tuning with LoRA, DPO, and GRPO.

Frequently Asked Questions about axolotl

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

FAQPage Schema
How do I configure LoRA and DPO for LLM fine-tuning?▼

You configure LoRA and DPO fine-tuning by defining your parameters in a YAML configuration file. This YAML-driven approach allows you to manage end-to-end LLM training, including QLoRA and GRPO RLHF flows, reproducibly across multiple backends.

Can I use YAML to fine-tune models with vLLM or Ollama backends?▼

Yes, YAML-driven LLM fine-tuning supports integration with multiple backends including vLLM, Ollama, llama.cpp, and MLX. You can specify your desired backend in the configuration to execute your fine-tuning plan across different environments.

What is GRPO and how does it apply to RLHF pipelines?▼

GRPO is a reinforcement learning from human feedback (RLHF) flow supported by this YAML-driven fine-tuning process. It allows ML engineers and researchers to configure policy enforcement and execute production-grade RLHF setups directly through YAML configurations.

How do I start fine-tuning a large language model with YAML configs?▼

To start fine-tuning, create a YAML config describing your model, dataset, and training plan. Running the trainer with this configuration initiates the YAML-driven LLM fine-tuning process for LoRA, DPO, or GRPO.

Does YAML-driven fine-tuning support policy controls and memory backends?▼

Yes, YAML-driven LLM fine-tuning supports policy enforcement and memory backends. These features are integrated directly into the configuration system to manage data pipelines and ensure reproducible, configurable fine-tuning.

When should I use QLoRA instead of standard LoRA for fine-tuning?▼

You choose between QLoRA and standard LoRA based on your memory constraints and hardware setup. Both adapter methods are fully supported within the YAML configuration, allowing you to optimize large language model fine-tuning for research pipelines or rapid prototyping.

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