axolotl

Name: axolotl
Availability: InStock
Author: Rawgrowth-Consulting

Fine-tune LLMs with YAML-config driven Axolotl workflows.

Updated Apr 23, 2026

Search Tags:#llm #fine-tuning #multimodal #yaml #lora #dpo #axolotl

Ready to run

npx skills add https://github.com/Rawgrowth-Consulting/rawclaw-agent --skill axolotl-rawgrowth-consulting

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/Rawgrowth-Consulting/rawclaw-agent/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/Rawgrowth-Consulting/rawclaw-agent --skill axolotl-rawgrowth-consulting

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Axolotl provides expert guidance to streamline fine-tuning of large language models using YAML-config driven workflows, enabling researchers and engineers to standardize experiments and reproduce results.

Core Features & Use Cases

YAML-config driven fine-tuning for LoRA/QLoRA, DPO/ORPO/GRPO, and multimodal setups.
Model-agnostic workflows across OpenAI, HuggingFace, and locally hosted models with robust dataset integration.
Use cases include rapid prototyping of RLHF, alignment experiments, and production-grade fine-tuning pipelines with persistent memory and cross-session recall.

Quick Start

Create a YAML config for your model and run the Axolotl trainer to start fine-tuning.

Frequently Asked Questions about axolotl

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

FAQPage Schema

How do I fine-tune a large language model using a YAML config?▼

To fine-tune an LLM using a YAML config, you define your model, dataset, and training parameters in a YAML file, then run the Axolotl trainer to execute the fine-tuning pipeline based on those configurations.

Can I use LoRA and DPO for fine-tuning multimodal models?▼

Yes, you can use LoRA, QLoRA, DPO, ORPO, and GRPO to fine-tune multimodal models. The workflow supports these alignment and parameter-efficient fine-tuning techniques directly through YAML configuration.

Do I need deepspeed and accelerate to run LLM fine-tuning pipelines?▼

Yes, deepspeed and accelerate are listed as core dependencies for running LLM fine-tuning pipelines, enabling distributed training and hardware acceleration when executing your YAML-driven experiments.

What is the best way to standardize and reproduce RLHF experiments?▼

The best way to standardize and reproduce RLHF experiments is by using YAML-config driven workflows, which allow you to rapidly prototype alignment experiments and maintain reproducible configurations across sessions.

Does this fine-tuning workflow support HuggingFace and locally hosted models?▼

Yes, the fine-tuning workflow supports model-agnostic operations across OpenAI, HuggingFace, and locally hosted models, providing robust dataset integration for your training pipelines.