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axolotl

Fine-tune large language models using Axolotl YAML configuration files.

Updated Sep 1, 2021
Search Tags:#llm#fine-tuning#multimodal#lora#qlora#dpo#axolotl
One-click install
npx skills add https://github.com/unclehowell/unclehowell --skill axolotl-unclehowell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/training/axolotl
Command: npx skills add https://github.com/unclehowell/unclehowell --skill axolotl-unclehowell

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Axolotl streamlines the complex process of fine‑tuning large language models, handling configuration intricacies, model selection, and advanced training techniques so you can focus on results instead of boilerplate.

Core Features & Use Cases

  • Extensive Model Support: Works with 100+ models and multimodal architectures.
  • Advanced Fine‑Tuning Methods: Provides ready‑to‑use configurations for LoRA, QLoRA, DPO, KTO, ORPO, and GRPO.
  • YAML‑Based Configurations: Simplifies experiment setup with clear, version‑controlled YAML files.
  • Use Case Example: Generate a LoRA‑enabled fine‑tuning run for a Llama‑3 model on a custom dataset in minutes.

Quick Start

Ask the axolotl skill to create a LoRA fine‑tuning configuration for a Llama‑3 model using 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 Llama-3 model with LoRA on a custom dataset?▼

Fine-tune a Llama-3 model with LoRA by generating a YAML configuration file that specifies your custom dataset and training parameters. Axolotl processes this YAML to execute the LoRA training run automatically.

Can I use QLoRA and DPO for large language model fine-tuning?▼

Yes, you can use QLoRA and DPO for large language model fine-tuning. Axolotl provides ready-to-use configurations supporting QLoRA, DPO, KTO, ORPO, and GRPO training methods.

What Python libraries are required to set up an LLM fine-tuning environment?▼

Setting up an LLM fine-tuning environment requires PyTorch, Transformers, datasets, PEFT, Accelerate, and DeepSpeed. These dependencies must be installed in your Python environment alongside Axolotl.

Does YAML-based configuration support multimodal dataset training?▼

Yes, YAML-based configuration supports multimodal dataset training. Axolotl applies these configurations across 100+ models and multimodal architectures to streamline complex training setups.

Why use Axolotl instead of manually configuring DeepSpeed and PEFT?▼

Use Axolotl instead of manually configuring DeepSpeed and PEFT to eliminate boilerplate. It abstracts configuration intricacies and model selection, letting you focus on results through version-controlled YAML files.

What are the limitations when applying GRPO to large language models?▼

Applying GRPO to large language models requires a Python environment equipped with PyTorch and DeepSpeed. Limitations depend on your hardware capacity to support the specific multimodal datasets and model architectures selected.

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