skills.rest
fikriaf
fikriafCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

axolotl

Fine-tune Large Language Models using YAML configurations and Axolotl.

Updated Apr 29, 2026
Search Tags:#model configuration#LLM fine-tuning#axolotl#neural network#large language model
One-click install
npx skills add https://github.com/fikriaf/agentos --skill axolotl-fikriaf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/fikriaf/agentos/tree/main/src/agentos/skills/mlops/training/axolotl
Command: npx skills add https://github.com/fikriaf/agentos --skill axolotl-fikriaf

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?

Axolotl addresses the challenge of fine-tuning Large Language Models (LLMs) by providing a comprehensive set of tools and resources, simplifying the process and enabling users to achieve high-quality results with ease.

Core Features & Use Cases

  • Fine-Tuning Assistance: Offers YAML configurations, 100+ models, and advanced techniques like LoRA, QLoRA, DPO, KTO, ORPO, and GRPO.
  • Multimodal Support: Allows integration with various data types, including text, images, and audio.
  • Efficient Configuration: Streamlines the setup process with a clear and concise configuration format.
  • Use Case: For researchers and developers looking to enhance their LLMs for specific tasks like text generation, machine translation, or code completion.

Quick Start

Trigger the 'axolotl' skill to fine-tune a specific LLM with your dataset.

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 YAML configurations?▼

You can fine-tune a large language model by defining your training parameters and dataset paths within a YAML configuration, which streamlines the setup process for customized LLM training. This approach supports over 100 models.

Can I use LoRA and QLoRA for efficient LLM fine-tuning?▼

Yes, LLM fine-tuning supports advanced techniques like LoRA, QLoRA, DPO, KTO, ORPO, and GRPO. These methods enable efficient customization and enhanced performance without requiring full parameter updates.

Does LLM fine-tuning support multimodal data like images and audio?▼

Yes, multimodal support allows integration with various data types during fine-tuning, including text, images, and audio. This enables researchers to build customized models across diverse data formats.

Do I need PyTorch and DeepSpeed to train large language models?▼

Yes, efficient training requires PyTorch, DeepSpeed, Accelerate, Transformers, Datasets, and PEFT. These dependencies facilitate optimized memory usage and distributed training for large language models.

What is the best way to customize a text generation model for specific tasks?▼

The best way is to use a fine-tuning framework that offers YAML configurations and advanced techniques like LoRA or DPO. This allows you to adapt pre-trained models for specific tasks such as code completion.

Related Skills

Explore More Software Engineering Skills→
projectedanxprojectedanx
1

simpo-training

Optimize Large Language Models with Simple Preference Optimization using PyTorch and transformers.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
JeffallanJeffallan
10.9k

fine-tuning-expert

Automate fine-tuning workflows for large language models using LoRA/QLoRA and PEFT methods.

Community
Advanced
huggingfacehuggingface
10.9k

hugging-face-model-trainer

Train and fine-tune language models on Hugging Face Jobs using TRL methods.

Official
Advanced