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unsloth

Fine-tune large language models with optimized LoRA and QLoRA workflows.

3|Updated Apr 21, 2026
Search Tags:#reinforcement learning#lora#qlora#unsloth#llm fine-tuning#vram optimization
One-click install
npx skills add https://github.com/DarkArty07/Aether-Agents --skill unsloth-darkarty07
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/DarkArty07/Aether-Agents/tree/main/home/skills/mlops/training/unsloth
Command: npx skills add https://github.com/DarkArty07/Aether-Agents --skill unsloth-darkarty07

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tuning large language models traditionally requires expensive high-VRAM GPUs and hours of training time, making custom model development inaccessible for individual developers, small teams, and researchers without cloud compute budgets.

Core Features & Use Cases

  • Optimized Fine-Tuning: Supports 2-5x faster LoRA and QLoRA fine-tuning for popular open-source LLMs including Llama, Qwen, Mistral, Gemma, and DeepSeek, with up to 70% less VRAM usage than standard frameworks.
  • Multi-Modal & RL Support: Enables fine-tuning for vision, text-to-speech, and reinforcement learning (RL) use cases, including GRPO and GSPO training for reasoning models.
  • Hardware Flexibility: Provides setup guides for consumer GPUs like the RTX 4070 Ti Super, AMD GPUs, and cloud providers like RunPod, plus VRAM requirement tables for every major model size.
  • Use Case Example: A developer with a 16GB VRAM desktop GPU can fine-tune a 7B parameter Qwen model on a custom customer support dataset in under an hour, no expensive cloud GPU required.

Quick Start

Use the unsloth skill to fine-tune a 7B Llama model on your custom dataset using 4-bit QLoRA on your local RTX 4070 Ti Super GPU.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune a Llama or Qwen model on a local 16GB VRAM GPU?▼

You can fine-tune a 7B parameter Llama or Qwen model on a 16GB VRAM GPU using 4-bit QLoRA optimization workflows. This approach reduces VRAM usage by up to 70% and accelerates training 2-5x, enabling custom model development in under an hour without expensive cloud compute.

Does unsloth support reinforcement learning workflows for reasoning models?▼

Yes, it supports reinforcement learning workflows for reasoning models, including GRPO and GSPO training. It enables fine-tuning for vision, text-to-speech, and RL use cases, eliminating high VRAM and long training time barriers for large language model development.

What is the best way to reduce VRAM usage during LLM fine-tuning?▼

Using optimized LoRA and QLoRA training workflows is the best way to reduce VRAM usage during LLM fine-tuning, achieving up to 70% less VRAM consumption than standard frameworks. It provides hardware-specific configuration guidance and VRAM requirement tables for every major model size.

Can I use consumer GPUs like the RTX 4070 Ti Super for training large language models?▼

Yes, you can use consumer GPUs like the RTX 4070 Ti Super for training large language models. It provides setup guides for consumer GPUs, AMD GPUs, and cloud providers like RunPod, making custom chat model training and domain adaptation accessible without cloud compute budgets.

How do I set up end-to-end model development and deployment after fine-tuning?▼

You can set up end-to-end model development and deployment by following its optimized fine-tuning workflows and inference engine integration instructions. It streamlines the process from custom dataset training on models like Mistral and DeepSeek to final reasoning model refinement and deployment.

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