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unsloth

Fine-tune LLMs with Unsloth using LoRA and QLoRA optimization.

6|3|Updated Jan 29, 2026
Search Tags:#llm#fine-tuning#memory optimization#unsloth#training speed
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill unsloth-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/unsloth
Command: npx skills add https://github.com/jonnabio/ace-framework --skill unsloth-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill offers expert guidance for fast and memory-efficient fine-tuning using Unsloth, optimizing training speed and memory usage.

Core Features & Use Cases

  • Fast Fine-Tuning: Achieve 2-5x faster training with 50-80% less memory.
  • LoRA/QLoRA Optimization: Leverage Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) for efficient fine-tuning.
  • Use Case: Fine-tune LLM models like gpt-oss, Llama, DeepSeek, TTS, Qwen, Mistral, Gemma, and Qwen3 for improved performance and efficiency.

Quick Start

To get started with Unsloth, install it with pip:

pip install unsloth

Then, you can fine-tune your LLM model using the following command:

unsloth fine-tune --model <model_name> --dataset <dataset_path>

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune LLM models faster with less memory?▼

You can fine-tune LLM models faster with less memory by using Unsloth to achieve 2-5x faster training speeds and 50-80% lower memory consumption through optimized LoRA and QLoRA techniques.

What is LoRA and QLoRA optimization for fine-tuning?▼

LoRA and QLoRA optimization are low-rank adaptation and quantized low-rank adaptation techniques that drastically reduce memory usage while fine-tuning LLMs, enabling efficient training without degrading model performance.

Can I use Unsloth to fine-tune Llama, DeepSeek, and Mistral models?▼

Yes, you can use Unsloth to fine-tune Llama, DeepSeek, Mistral, Qwen, Gemma, and TTS models, providing optimized training speed and memory efficiency across these supported LLM architectures.

What dependencies do I need to install for efficient LLM training?▼

You need to install unsloth, torch, transformers, trl, datasets, and peft as dependencies to enable efficient LLM training, reinforcement learning, and memory optimization within your environment.

How do I start fine-tuning an LLM using Unsloth?▼

To start fine-tuning an LLM using Unsloth, install the package via pip and execute the command 'unsloth fine-tune --model <model_name> --dataset <dataset_path>' to initiate training.

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