skills.rest
huidge
huidgeCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

unsloth

Fine-tune LLMs with Unsloth using LoRA/QLoRA and dynamic quantization.

2|2|Updated Apr 16, 2026
Search Tags:#llm#fine-tuning#reinforcement-learning#qlora#loRA#memory-efficiency#unsloth
One-click install
npx skills add https://github.com/huidge/hermes-skills --skill unsloth-huidge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/huidge/hermes-skills/tree/main/mlops/training/unsloth
Command: npx skills add https://github.com/huidge/hermes-skills --skill unsloth-huidge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes references (resource) components.

What problem does it solve?

Fine-tuning LLMs locally often requires significant compute and memory. This Skill provides expert guidance and best practices to perform fast, memory-efficient fine-tuning and reinforcement learning with Unsloth, enabling repeatable workflows.

Core Features & Use Cases

  • End-to-end fine-tuning and RL workflows with LoRA/QLoRA optimizations to reduce VRAM usage.
  • Local-first tooling: runs on Linux/Windows with Docker, Colab, or Kaggle, supporting diverse GPUs from consumer to enterprise.
  • Guided best practices for selecting models, datasets, and hyperparameters to achieve faster iterations and reliable results.

Quick Start

Install Unsloth locally and begin with the beginner guides to fine-tune a small model on your dataset.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune an LLM with QLoRA to reduce VRAM usage?▼

You can fine-tune an LLM with reduced VRAM using QLoRA through Unsloth's dynamic quantization workflows. This skill coordinates end-to-end LoRA and QLoRA training to enable memory-efficient fine-tuning on local devices and cloud environments.

Can I run reinforcement learning training on my local GPU with PyTorch?▼

Yes, you can run reinforcement learning training on local GPUs using PyTorch with Unsloth. It coordinates RL workflows alongside LoRA and QLoRA optimizations, supporting diverse consumer and enterprise GPUs across Linux, Windows, Colab, and Kaggle.

What is the best way to set up memory-efficient LLM fine-tuning in Colab?▼

The best way to set up memory-efficient LLM fine-tuning in Colab is using the Unsloth toolchain with PyTorch and Transformers. It provides dynamic quantization workflows that significantly reduce memory requirements for faster iterations.

Do I need Docker to fine-tune models locally with Unsloth?▼

You do not necessarily need Docker to fine-tune models locally, as Unsloth runs directly on Linux and Windows. However, using Docker is supported as a local-first tooling option to ensure a repeatable and consistent development environment.

Why does standard LLM fine-tuning require so much compute and memory?▼

Standard LLM fine-tuning requires significant compute and memory because it updates all model weights across massive parameter spaces. Unsloth solves this by applying dynamic quantization and LoRA optimizations to drastically reduce VRAM usage and accelerate training.

What dependencies are required to start fine-tuning with Unsloth?▼

To start fine-tuning with Unsloth, you need a standard Python development environment with PyTorch, Transformers, Datasets, TRL, and PEFT installed. These dependencies form the complete toolchain for running the guided fine-tuning workflows.

Related Skills

Explore More Software Engineering Skills→
carterwayneskhizeinecarterwayneskhizeine
19

unsloth

Plan LoRA/QLoRA fine-tuning workflows for transformer models under constrained VRAM.

Community
Intermediate
ScientiaCapitalScientiaCapital
1

model-deployment

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

Community
Advanced
cloudlinesscloudliness

peft-fine-tuning

Fine-tune 7B-70B language models with LoRA, QLoRA, AdaLoRA, and IA3.

Community
Advanced
huggingfacehuggingface
10.9k

hugging-face-model-trainer

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

Official
Advanced