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axolotl

Coordinate end-to-end LLM fine-tuning with YAML-configured workflows.

1|Updated Jan 31, 2026
Search Tags:#llm#fine-tuning#multimodal#yaml#dpo#grpo#loRA
One-click install
npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill axolotl-monjyu1101
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/Monjyu1101/AiDiy2026/tree/main/backend_hermes/skills/mlops/training/axolotl
Command: npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill axolotl-monjyu1101

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Axolotl provides a structured framework to fine-tune large language models using YAML-based configurations, enabling reproducible experiments and rapid iteration.

Core Features & Use Cases

  • YAML-driven fine-tuning for LoRA, DPO, GRPO across many models and modalities.
  • End-to-end automation of setup, preprocessing, and training orchestration for reproducible experiments.
  • Use Case: teams can rapidly prototype policy-aware fine-tuning by swapping the YAML blocks to test different adapters and objectives.

Quick Start

Run the setup script to install dependencies and initialize Axolotl for YAML-based 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 an LLM with YAML configs for LoRA and DPO?▼

You can fine-tune an LLM using YAML configs for LoRA and DPO by defining training parameters in a YAML file. This approach enables reproducible experiments and automates the end-to-end setup, preprocessing, and training orchestration across many models.

What's the best way to automate reproducible LLM fine-tuning pipelines?▼

The best way to automate reproducible LLM fine-tuning is using a YAML-configured framework that coordinates end-to-end workflows. This enables scalable and repeatable training pipelines from initial setup to final deployment.

Can I use deepspeed and accelerate for multimodal LLM fine-tuning?▼

Yes, you can use deepspeed and accelerate for multimodal LLM fine-tuning. The framework supports multimodal setups alongside LoRA, DPO, and GRPO objectives, utilizing these dependencies for scalable and optimized training.

Does YAML-based fine-tuning support GRPO and peft adapters?▼

YAML-based fine-tuning fully supports GRPO and peft adapters. You can rapidly prototype policy-aware fine-tuning by swapping YAML blocks to test different adapters and objectives across various models.

Do I need torch and transformers installed to run YAML-configured LLM training?▼

Yes, you need torch and transformers installed along with datasets, peft, accelerate, and deepspeed. These runtime dependencies are required to execute the YAML-configured LLM training workflows.

Why use YAML configurations instead of scripts for LLM fine-tuning?▼

Using YAML configurations for LLM fine-tuning provides a structured framework that enables rapid iteration and reproducible experiments. Swapping YAML blocks allows teams to easily test different adapters and objectives without modifying scripts.

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