skills.rest
heysuhas
heysuhasCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

axolotl

Manage YAML-based configuration for fine-tuning LLMs with PyTorch and HuggingFace.

2|1|Updated Jul 14, 2026
Search Tags:#llm#fine-tuning#yaml#lora#huggingface#deepspeed
One-click install
npx skills add https://github.com/heysuhas/hermes_cli --skill axolotl-heysuhas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axolotl
Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/mlops/training/axolotl
Command: npx skills add https://github.com/heysuhas/hermes_cli --skill axolotl-heysuhas

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 simplifies the complex process of fine-tuning Large Language Models by providing a unified, configuration-driven approach that handles diverse training methods and model architectures without requiring deep code modifications.

Core Features & Use Cases

  • Unified Configuration: Manage LoRA, QLoRA, DPO, and GRPO training runs using simple YAML files.
  • Broad Model Support: Access support for over 100 models and various dataset formats including instruction, chat, and pre-training.
  • Use Case: A researcher needs to fine-tune a Llama-3 model on a custom dataset using QLoRA. Instead of writing custom training loops, they define the model path, dataset, and LoRA parameters in a YAML file, and Axolotl handles the tokenization, training, and checkpointing.

Quick Start

Use the axolotl skill to launch a training run using the configuration file named train_config.yaml.

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 QLoRA without writing custom training loops?▼

Fine-tune a Llama-3 model with QLoRA by defining the model path, dataset, and LoRA parameters in a YAML configuration file. The system processes the YAML to automatically handle tokenization, training execution, and checkpointing without custom code.

Can I use DeepSpeed for distributed LLM training through a YAML configuration interface?▼

Yes, you can use DeepSpeed for distributed LLM training through a YAML configuration interface. The system integrates with PyTorch and DeepSpeed to manage distributed training execution alongside model loading and tokenization.

Does this approach support DPO and GRPO training paradigms for HuggingFace models?▼

Yes, this configuration-driven approach supports DPO and GRPO training paradigms for HuggingFace models. It handles diverse training methods across over 100 supported model architectures using simple YAML files.

What is the best way to manage supervised fine-tuning and LoRA configurations for large language models?▼

The best way to manage supervised fine-tuning and LoRA configurations is using a unified YAML-based configuration interface. This approach handles model loading, tokenization, and diverse training methods without requiring deep code modifications.

Do I need PyTorch and HuggingFace dependencies installed to run QLoRA training with YAML configs?▼

Yes, you need PyTorch and HuggingFace dependencies installed to run QLoRA training with YAML configs. Required dependencies include torch, transformers, datasets, peft, accelerate, and deepspeed to successfully execute the training pipeline.

What dataset formats are supported when fine-tuning LLMs through YAML configurations?▼

Supported dataset formats for fine-tuning LLMs through YAML configurations include instruction, chat, and pre-training formats. The system processes these diverse dataset types automatically during the training execution.

Related Skills

Explore More Software Engineering Skills→
ricablericable

fine-tuning-with-trl

Fine-tune large language models with TRL for supervised and preference alignment.

Community
Advanced
ovachieverovachiever
52

transformers

Load, infer, and fine-tune pre-trained transformer models across modalities.

Community
Advanced
Ced3-hanCed3-han

fine-tuning-with-trl

Fine-tune LLMs with TRL reinforcement learning and HuggingFace Transformers.

Community
Advanced
zeronx798zeronx798

axolotl

Fine-tune Large Language Models using YAML configurations and automated training workflows.

Community
Advanced